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NeurIPS 2020

379 minute read

Published:

连续表面嵌入
Continuous Surface Embeddings
Natalia Neverova (Facebook AI Research) · David Novotny (Facebook AI Research) · Marc Szafraniec (Facebook AI Research) · Vasil Khalidov (Facebook AI Research) · Patrick Labatut (Facebook AI Research) · Andrea Vedaldi (University of Oxford / Facebook AI Research)

通过准确性与不确定性优化来改善模型校准
Improving model calibration with accuracy versus uncertainty optimization
Ranganath Krishnan (Intel Labs) · Omesh Tickoo (Intel)

通过自适应生成很少的图像
Few-shot Image Generation via Self-Adaptation
Yijun Li (Adobe Research) · Richard Zhang (Adobe) · Jingwan (Cynthia) Lu (Adobe Research) · Eli Shechtman (Adobe Research, US)

Hausdorff维数,重尾和神经网络中的泛化
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks
Umut Simsekli (Institut Polytechnique de Paris/ University of Oxford) · Ozan Sener (Intel Labs) · George Deligiannidis (Oxford) · Murat Erdogdu (University of Toronto)

广域神经网络中SGD的混沌定量传播
Quantitative Propagation of Chaos for SGD in Wide Neural Networks
Valentin De Bortoli (ENS Paris-Saclay) · Alain Durmus (ENS Paris Saclay) · Xavier Fontaine (ENS Paris-Saclay) · Umut Simsekli (Institut Polytechnique de Paris/ University of Oxford)

执行预测的随机优化
Stochastic Optimization for Performative Prediction
Celestine Mendler-Dünner (UC Berkeley) · Juan Perdomo (University of California, Berkeley) · Tijana Zrnic (UC Berkeley) · Moritz Hardt (University of California, Berkeley)

高斯噪声注入中的显式正则化
Explicit Regularisation in Gaussian Noise Injections
Alexander Camuto (University of Oxford & The Alan Turing Institute) · Matthew Willetts (University of Oxford) · Umut Simsekli (Institut Polytechnique de Paris/ University of Oxford) · Stephen J Roberts (University of Oxford) · Chris C Holmes (University of Oxford)

对偶工具变量回归
Dual Instrumental Variable Regression
Krikamol Muandet (Max Planck Institute for Intelligent Systems) · Arash Mehrjou (Max Planck Institute) · Si Kai Lee (Chicago Booth School of Business) · Anant Raj (Max Planck Institute for Intelligent Systems)

最大熵对抗数据增强以提高泛化性和鲁棒性
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness
Long Zhao (Rutgers University) · Ting Liu (Google) · Xi Peng (University of Delaware) · Dimitris Metaxas (Rutgers University)

图神经网络的强增量选区解析
Strongly Incremental Constituency Parsing with Graph Neural Networks
Kaiyu Yang (Princeton University) · Jia Deng (Princeton University)

AdaBelief优化器:根据观察渐变中的信念调整步长
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Juntang Zhuang (Yale University) · Tommy Tang (University of Illinois Urbana-Champaign) · Sekhar C Tatikonda (Yale University) · Nicha Dvornek (Yale University) · Yifan Ding (University of Central Florida) · Xenophon Papademetris (Yale University) · James Duncan (Yale University)

私有多元均值和协方差估计的简单实用算法
A Simple and Practical Algorithm for Private Multivariate Mean and Covariance Estimation
Sourav Biswas (University of Waterloo) · Yihe Dong (Microsoft) · Gautam Kamath (University of Waterloo) · Jonathan Ullman (Northeastern University)

离散高斯离散隐私
The Discrete Gaussian for Differential Privacy
Clément L Canonne (IBM Research) · Gautam Kamath (University of Waterloo) · Thomas Steinke (IBM Almaden)

高维分布的私有身份测试
Private Identity Testing for High-Dimensional Distributions
Clément L Canonne (IBM Research) · Gautam Kamath (University of Waterloo) · Audra McMillan (Northeastern/Boston University) · Jonathan Ullman (Northeastern University) · Lydia Zakynthinou (Northeastern University)

树!我不是树!我是低维双曲嵌入
Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding
Rishi S Sonthalia (University of Michigan) · Anna Gilbert (University of Michigan)

在不拆分数据的情况下学习内核测试
Learning Kernel Tests Without Data Splitting
Jonas Kübler (MPI for Intelligent Systems, Tübingen) · Wittawat Jitkrittum (Max Planck Institute for Intelligent Systems) · Bernhard Schölkopf (MPI for Intelligent Systems) · Krikamol Muandet (Max Planck Institute for Intelligent Systems)

生成视图综合:从单视图语义到新颖视图图像
Generative View Synthesis: From Single-view Semantics to Novel-view Images
Tewodros Amberbir Habtegebrial (Technische Universität Kaiserslautern) · Varun Jampani (Google) · Orazio Gallo (NVIDIA Research) · Didier Stricker (DFKI)

有理神经网络
Rational neural networks
Nicolas Boulle (University of Oxford) · Yuji Nakatsukasa (University of Oxford) · Alex J Townsend (Cornell University)

节点访问受限的图神经网络的对抗攻击
Adversarial Attack on Graph Neural Networks with Limited Node Access
Jiaqi Ma (University of Michigan) · Shuangrui Ding (University of Michigan) · Qiaozhu Mei (University of Michigan)

量化表示学习中出现的视觉概念的可学习性和可描述性
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
Iro Laina (University of Oxford) · Ruth Fong (University of Oxford) · Andrea Vedaldi (Facebook AI Research and University of Oxford)

时空对应作为对比随机游走
Space-Time Correspondence as a Contrastive Random Walk
Allan Jabri (UC Berkeley) · Andrew Owens (UC Berkeley) · Alexei Efros (UC Berkeley)

使用逆向RL重新标记经验:改进政策的后见之明
Relabeling Experience with Inverse RL: Hindsight Inference for Policy Improvement
Ben Eysenbach (Carnegie Mellon University) · XINYANG GENG (UC Berkeley) · Sergey Levine (UC Berkeley) · Russ Salakhutdinov (Carnegie Mellon University)

用于视频表示学习的自指导式联合训练
Self-supervised Co-Training for Video Representation Learning
Tengda Han (University of Oxford) · Weidi Xie (University of Oxford) · Andrew Zisserman (DeepMind & University of Oxford)

Rel3D:3D中的最小对比基准或接地空间关系
Rel3D: A Minimally Contrastive Benchmark or Grounding Spatial Relations in 3D
Ankit Goyal (Princeton University) · Kaiyu Yang (Princeton University) · Dawei Yang (University of Michigan) · Jia Deng (Princeton University)

归一化对于训练深度神经网络必不可少吗?
Is normalization indispensable for training deep neural network?
Jie Shao (Fudan University) · Kai Hu (Carnegie Mellon University) · Changhu Wang (ByteDance.Inc) · Xiangyang Xue (Fudan University) · Bhiksha Raj (Carnegie Mellon University)

二值神经网络的高效精确验证
Efficient Exact Verification of Binarized Neural Networks
Kai Jia (MIT) · Martin Rinard (MIT)

ConvBERT:通过基于跨度的动态卷积改进BERT
ConvBERT: Improving BERT with Span-based Dynamic Convolution
Zi-Hang Jiang (National University of Singapore) · Weihao Yu (National University of Singapore) · Daquan Zhou (National University of Singapore) · Yunpeng Chen (Yitu Technology) · Jiashi Feng (National University of Singapore) · Shuicheng Yan (National University of Singapore)

分布外检验的价值:以古德哈特定律为例
On the Value of Out-of-Distribution Testing: An Example of Goodhart’s Law
Damien Teney (University of Adelaide) · Ehsan Abbasnejad (University of Adelaide) · Kushal Kafle (Rochester Institute of Technology) · Robik Shrestha (Rochester Institute of Technology) · Christopher Kanan (PAIGE.AI / RIT / CornellTech) · Anton van den Hengel (University of Adelaide)

使用多模式自我监督从头开始标记未标记的视频
Labelling unlabelled videos from scratch with multi-modal self-supervision
Yuki Asano (University of Oxford) · Mandela Patrick (University of Oxford) · Christian Rupprecht (University of Oxford) · Andrea Vedaldi (University of Oxford / Facebook AI Research)

理智检查修剪方法:随机彩票可以赢得大奖
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
Jingtong Su (Peking University) · Yihang Chen (Peking University) · Tianle Cai (Peking University) · Tianhao Wu (Peking University) · Ruiqi Gao (Peking University) · Liwei Wang (Peking University) · Jason Lee (Princeton University)

基集较差的分子图卷积和分子波函数的等价性
On the equivalence of molecular graph convolution and molecular wave function with poor basis set
Masashi Tsubaki (National Institute of Advanced Industrial Science and Technology (AIST)) · Teruyasu Mizoguchi (University of Tokyo)

通过动态图学习生成3D零件
Generative 3D Part Assembly via Dynamic Graph Learning
佳磊 黄 (Peking University) · Guanqi Zhan (Peking University) · Qingnan Fan (Stanford University) · Kaichun Mo (Stanford University) · Lin Shao (Stanford University) · Baoquan Chen (Shandong University) · Leonidas J Guibas (stanford.edu) · Hao Dong (Peking University)

先知注意力:预测注意力和未来注意力,以改善图像字幕
Prophet Attention: Predicting Attention with Future Attention for Improved Image Captioning
Fenglin Liu (Peking University) · Xuancheng Ren (Peking University) · Xian Wu (Tencent Medical AI Lab) · Shen Ge (Tencent Medical AI Lab) · Wei Fan (Tencent) · Yuexian Zou (Peking University) · Xu Sun (Peking University)

启发式对抗域适应
Heuristic Adversarial Domain Adaptation
shuhao cui (ict cas) · Xuan Jin (Alibaba Turing Lab, Alibaba Group) · Shuhui Wang (VIPL,ICT,Chinese academic of science) · Yuan He (Alibaba Group) · Qingming Huang (University of Chinese Academy of Sciences)

核条件均值嵌入的度量理论方法
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings
Junhyung Park (MPI for Intelligent Systems, Tübingen) · Krikamol Muandet (Max Planck Institute for Intelligent Systems)

计划:从自然推断的规范中进行可靠的程序学习
PLANS: Robust Program Learning from Neurally Inferred Specifications
Raphaël Dang-Nhu (ETH Zürich)

AOT:用于面部交换的外观最佳运输模型
AOT: Appearance Optimal Transport Model for Face Swapping
Hao Zhu (Anhui University) · Chaoyou Fu (Institute of Automation, Chinese Academy of Sciences) · Qianyi Wu (Sensetime) · Wayne Wu (Tsinghua University) · Chen Qian (SenseTime) · Ran He (NLPR, CASIA)

归一化流的改进变分贝叶斯系统发生推断
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows
Cheng Zhang (Peking University)

替代物总价值最大化的适应性复杂性
The Adaptive Complexity of Maximizing a Gross Substitutes Valuation
Ron Kupfer (The Hebrew University of Jerusalem) · Sharon Qian (Harvard) · Eric Balkanski (Harvard University) · Yaron Singer (Harvard University)

广泛的神经网络中的知识蒸馏:风险界限,数据效率和不完善的老师
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher
Guangda Ji (Peking University) · Zhanxing Zhu (Peking University)

可能性后悔:变分自动编码器的分布外检测得分
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
Zhisheng Xiao (The University of Chicago) · Qing Yan (University of Chicago) · Yali Amit (University of Chicago)

基于树模型的贝叶斯概率数值积分
Bayesian Probabilistic Numerical Integration with Tree-Based Models
Harrison Zhu (Imperial College London) · Xing Liu (University of Cambridge) · Ruya Kang (ETH Zurich) · Zhichao Shen (University of Oxford) · Seth Flaxman (Imperial College London) · Francois-Xavier Briol (University of Cambridge)

每个视图都很重要:具有混合圆柱球体素化的3D对象检测中的跨视图一致性
Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization
Qi Chen (Johns Hopkins University) · Lin Sun (Samsung, Stanford, HKUST) · Ernest Cheung (Samsung) · Alan Yuille (Johns Hopkins University)

通过无监督学习对RL进行有效的探索
Provably Efficient Exploration for RL with Unsupervised Learning
Fei Feng (University of California, Los Angeles) · Ruosong Wang (Carnegie Mellon University) · Wotao Yin (Alibaba US, DAMO Academy) · Simon Du (Institute for Advanced Study) · Lin Yang (UCLA)

具有形状和时间多样性的概率时间序列预测
Probabilistic Time Series Forecasting with Shape and Temporal Diversity
Vincent LE GUEN (CNAM, Paris, France) · Nicolas THOME (Cnam (Conservatoire national des arts et métiers))

使用自动编码的可变贝叶斯决策
Decision-Making with Auto-Encoding Variational Bayes
Romain Lopez (UC Berkeley) · Pierre Boyeau (UC Berkeley) · Nir Yosef (UC Berkeley) · Michael Jordan (UC Berkeley) · Jeffrey Regier (University of Michigan)

RetroXpert:像化学家一样分解逆合成预测
RetroXpert: Decompose Retrosynthesis Prediction like A Chemist
Chaochao Yan (The University of Texas at Arlington) · Qianggang Ding (Tsinghua University) · Peilin Zhao (Tencent AI Lab) · Shuangjia Zheng (SUN YAT-SEN UNIVERSITY) · JINYU YANG (The University of Texas at Arlington) · Yang Yu (Tencent AI Lab) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

DVERGE:多样化漏洞,增强了乐团的强大生成能力
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
Huanrui Yang (Duke University) · Jingyang Zhang (Duke University) · Hongliang Dong (Duke University) · Nathan Inkawhich (Duke University) · Andrew Gardner (Radiance Technologies) · Andrew Touchet (Radiance Technologies) · Wesley Wilkes (Radiance Technologies) · Heath Berry (Radiance Technologies) · Hai Li (Duke University)

CaSPR:学习规范时空点云表示
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
Davis Rempe (Stanford University) · Tolga Birdal (Technical University of Munich) · Yongheng Zhao (University of Padova) · Zan Gojcic (ETH Zürich) · Srinath Sridhar (Stanford University) · Leonidas J Guibas (stanford.edu)

不确定性学习的零镜头语义分割
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation
Ping Hu (Boston University) · Stan Sclaroff (Boston University) · Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)

面向任务的特征提取
Task-Oriented Feature Distillation
Linfeng Zhang (Tsinghua University) · Yukang Shi (Xi’an Jiaotong University) · Zuoqiang Shi (Tsinghua University) · Kaisheng Ma (Tsinghua University) · Chenglong Bao (Tsinghua university)

从随机缺失的反馈中获得信息理论反事实学习
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
Zifeng Wang (Tsinghua-Berkeley Shenzhen Institute, Tsinghua University) · Xi Chen (Tencent) · Rui Wen (Tencent) · Shao-Lun Huang (Tsinghua-Berkeley Shenzhen Institute) · Ercan E Kuruoglu (Tsinghua-Berkeley Shenzhen Institute) · Yefeng Zheng (Tencent)

用于多重校正的均匀收敛的样本复杂度
Sample Complexity of Uniform Convergence for Multicalibration
Eliran Shabat (Tel-Aviv University) · Lee Cohen (Tel Aviv University) · Yishay Mansour (Tel Aviv University / Google)

如果神经网络具有SVD,该怎么办?
What if Neural Networks had SVDs?
Alexander Mathiasen (Aarhus University) · Frederik Hvilshøj (Aarhus University) · Jakob Rødsgaard Jørgensen (Aarhus University) · Anshul Nasery (Indian Institute of Technology) · Davide Mottin (Aarhus University)

用于领域自适应对象Re-ID的具有混合内存的自定进度对比学习
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
Yixiao Ge (The Chinese University of Hong Kong) · Dapeng Chen (The Chinese University of Hong Kong) · Feng Zhu (SenseTime Research) · Rui Zhao (The Chinese University of Hong Kong) · hongsheng Li (cuhk)

从正和未标记的数据中进行任意正向学习
Learning from Positive and Unlabeled Data with Arbitrary Positive Shift
Zayd Hammoudeh (University of Oregon) · Daniel Lowd (University of Oregon)

CTR预测中用户行为建模的卡尔曼滤波注意
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
Hu Liu (JD.com) · Jing LU (Business Growth BU JD.com) · Xiwei Zhao (JD.com) · Sulong Xu (JD.com) · Hao Peng (JD.com) · Yutong Liu (JD.com) · Zehua Zhang (JD.com) · Jian Li (JD.com) · Junsheng Jin (JD.com) · Yongjun Bao (JD.com) · Weipeng Yan (JD.com)

使用分散的专家混合技术进行大型神经网络的众包培训
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts
Maksim Riabinin (Yandex, Higher School of Economics) · Anton Gusev (none)

对抗性强的少量学习:元学习方法
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
Micah Goldblum (University of Maryland) · Liam Fowl (University of Maryland) · Tom Goldstein (University of Maryland)

基于人解析的纹理通过跨视图一致性从单个图像到3D人体的转移
Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency
Fang Zhao (Inception Institute of Artificial Intelligence) · Shengcai Liao (Inception Institute of Artificial Intelligence) · Kaihao Zhang (Australian National University) · Ling Shao (Inception Institute of Artificial Intelligence)

一比特监督图像分类
One-bit Supervision for Image Classification
hengtong hu (Hefei University of Technology) · Lingxi Xie (Huawei Noah’s Ark Lab) · Zewei Du (Huawei Noah’s Ark Lab) · Richang Hong (Hefei University of Technology) · Qi Tian (Huawei Noah’s Ark Lab)

从理论上理解为什么sgd在深度学习中比亚当更好地推广
Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning
Pan Zhou (Salesforce) · Jiashi Feng (National University of Singapore) · Chao Ma (Princeton University) · Caiming Xiong (Salesforce) · Steven Chu Hong Hoi (Salesforce) · Weinan E (Princeton University)

RNNPool:用于RAM约束推理的高效非线性池
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Oindrila Saha (Microsoft Research) · Aditya Kusupati (University of Washington) · Harsha Vardhan Simhadri (Microsoft Research) · Manik Varma (Microsoft Research India) · Prateek Jain (Microsoft Research)

自我监督的关系探索
Self-Supervised Relationship Probing
Jiuxiang Gu (Adobe Research) · Jason Kuen (Adobe Research) · Shafiq Joty (Nanyang Technological University) · Jianfei Cai (Monash University) · Vlad Morariu (Adobe Research) · Handong Zhao (Adobe Research) · Tong Sun (Adobe Research)

可靠的神经网络修剪的科学控制
Scientific Control for Reliable Neural Network Pruning
Yehui Tang (Peking University) · Yunhe Wang (Huawei Noah’s Ark Lab) · Yixing Xu (Huawei Noah’s Ark Lab) · Dacheng Tao (University of Sydney) · Chunjing XU (Huawei Technologies) · Chao Xu (Peking University) · Chang Xu (University of Sydney)

3D点云的组上下文编码
Group Contextual Encoding for 3D Point Clouds
Xu Liu (The University of Tokyo) · Chengtao Li (MIT) · Jian Wang (Carnegie Mellon University) · Jingbo Wang (Peking University) · Boxin Shi (Peking University) · Xiaodong He (JD AI research)

一键无监督域自适应的对抗式挖掘
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
Yawei Luo (Zhejiang University) · Ping Liu (UTS) · Tao Guan (Huazhong University of Science and Technology) · Junqing Yu (Huazhong University of Science & Technology) · Yi Yang (UTS)

过滤器中的修剪过滤器
Pruning Filter in Filter
Fanxu Meng (Harbin Institute of Technology, Shenzhen) · Hao Cheng (Tencent) · Ke Li (Tencent) · Huixiang Luo (Tencent) · Xiaowei Guo (Tencent Youtu Lab) · Guangming Lu (Harbin Institute of Technology, Shenzhen) · Xing Sun (Tencent)

通过自我监督的球形CNN学习定向表面
Learning to Orient Surfaces by Self-supervised Spherical CNNs
Riccardo Spezialetti (University of Bologna) · Federico Stella (Università di Bologna) · Marlon Marcon (Federal University of Technology - Paraná) · Luciano Silva (UFPR) · Samuele Salti (University of Bologna) · Luigi Di Stefano (University of Bologna)

Beta表示形式:从另一个角度看待行人检测
Beta Representation: Looking into Pedestrian Detection from Another Perspective
Zixuan Xu (Peking University) · Banghuai Li (Megvii) · Ye Yuan (Megvii) · Anhong Dang (Peking University)

基于节点重要性的自适应组稀疏正则化的持续学习
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization
Sangwon Jung (SKKU) · Hongjoon Ahn (Sunkyunkwan University) · Sungmin Cha (Sungkyunkwan University) · Taesup Moon (Sungkyunkwan University (SKKU))

HOI分析:集成和分解人与对象的交互
HOI Analysis: Integrating and Decomposing Human-Object Interaction
Yong-Lu Li (Shanghai Jiao Tong University) · Xinpeng Liu (Shanghai Jiao Tong University) · Xiaoqian Wu (Shanghai Jiao Tong University) · Yizhuo Li (Shanghai Jiao Tong University) · Cewu Lu (Shanghai Jiao Tong University)

通过顺序蒙特卡洛进行广义贝叶斯滤波
Generalised Bayesian Filtering via Sequential Monte Carlo
Ayman Boustati (University of Warwick) · Omer Deniz Akyildiz (University of Warwick) · Theodoros Damoulas (University of Warwick & The Alan Turing Institute) · Adam Johansen (University of Warwick)

用于物体检测中分类和定位的基于等级的平衡损失函数
A Ranking-based, Balanced Loss Function for Both Classification and Localisation in Object Detection
Kemal Oksuz (Middle East Technical University) · Baris Can Cam (Roketsan) · Emre Akbas (Middle East Technical University) · Sinan Kalkan (Middle East Technical University)

StratLearner:学习社交网络中防止错误信息的策略
StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks
Guangmo Tong (University of Delaware)

超出常规范围的PAC-Bayes分析
PAC-Bayes Analysis Beyond the Usual Bounds
Omar Rivasplata (DeepMind & UCL) · Ilja Kuzborskij (DeepMind) · Csaba Szepesvari (DeepMind / University of Alberta) · John Shawe-Taylor (UCL)

三角图的快速灵活的时间点处理
Fast and Flexible Temporal Point Processes with Triangular Maps
Oleksandr Shchur (Technical University of Munich) · Nicholas Gao (Technical University of Munich) · Marin Biloš (Technical University of Munich) · Stephan Günnemann (Technical University of Munich)

残余力控制,用于模仿人类行为和扩展运动综合
Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis
Ye Yuan (Carnegie Mellon University) · Kris Kitani (Carnegie Mellon University)

使用光滑凸包络的随机逼近的有限样本分析
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes
Zaiwei Chen (Georgia Institute of Technology) · Siva Theja Maguluri (Georgia Institute of Technology) · Sanjay Shakkottai (University of Texas at Austin) · Karthikeyan Shanmugam (IBM Research, NY)

使用CART进行高维学习
High-Dimensional Learning with CART
Jason Klusowski (Princeton University)

通过学习与对象互动来了解对象
Learning About Objects by Learning to Interact with Them
Martin Lohmann (Allen Institute for Artificial Intelligence) · Jordi Salvador (Allen Institute for AI) · Aniruddha Kembhavi (Allen Institute for Artificial Intelligence (AI2)) · Roozbeh Mottaghi (Allen Institute for Artificial Intelligence)

Softmax深双确定性策略梯度
Softmax Deep Double Deterministic Policy Gradients
Ling Pan (Tsinghua University) · Qingpeng Cai (Alibaba Group) · Longbo Huang (IIIS, Tsinghua Univeristy)

LAPAR:线性组装的像素自适应回归网络,可用于超高分辨率和单幅图像
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond
Wenbo Li (Chinese University of Hong Kong) · Kun Zhou (Shenzhen SmartMore Technology Co., Ltd.) · Lu Qi (The Chinese University of Hong Kong) · Nianjuan Jiang (Shenzhen SmartMore Technology Co., Ltd.) · Jiangbo Lu (Shenzhen SmartMore Technology Co., Ltd.) · Jiaya Jia (CUHK)

展开针对盲超分辨率的交替优化
Unfolding the Alternating Optimization for Blind Super Resolution
zhengxiong luo (中国科学院自动化所) · Yan Huang (CRIPAC, CASIA) · Shang Li (CASIA) · Liang Wang (NLPR, China) · Tieniu Tan (Chinese Academy of Sciences)

具有挑战性的SATNet及其解决符号接地问题的能力
Challenging SATNet and its Ability to Solve the Symbol Grounding Problem
Oscar Chang (Columbia University) · Lampros Flokas (Columbia University) · Hod Lipson (Columbia University) · Michael Spranger (Sony)

用于文本引导图像处理的轻量级生成对抗网络
Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation
Bowen Li (University of Oxford) · Xiaojuan Qi (The University of Hong Kong) · Philip Torr (University of Oxford) · Thomas Lukasiewicz (University of Oxford)

训练深度神经网络的实用拟牛顿方法
Practical Quasi-Newton Methods for Training Deep Neural Networks
Donald Goldfarb (Columbia University) · Yi Ren (Columbia University) · Achraf Bahamou (Columbia University)

RANet:用于语义分割的区域注意网络
RANet: Region Attention Network for Semantic Segmentation
Dingguo Shen (Shenzhen University) · Yuanfeng Ji (City University of Hong Kong) · Ping Li (The Hong Kong Polytechnic University) · Yi Wang (Shenzhen University) · Di Lin (Tianjin University)

HM-ANN:异质内存上的有效十亿点最近邻居搜索
HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory
Jie Ren (University of California, Merced) · Minjia Zhang (Microsoft) · Dong Li (University of California, Merced)

在线适应野外一致的网格重建
Online Adaptation for Consistent Mesh Reconstruction in the Wild
Xueting Li (University of California, Merced) · Sifei Liu (NVIDIA) · Shalini De Mello (NVIDIA) · Kihwan Kim (NVIDIA) · Xiaolong Wang (UCSD/UC Berkeley) · Ming-Hsuan Yang (Google / UC Merced) · Jan Kautz (NVIDIA)

多关系有序和递归超图的神经信息传递
Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs
Naganand Yadati (Indian Institute of Science)

PIE-NET:点云边缘的参数推断
PIE-NET: Parametric Inference of Point Cloud Edges
Xiaogang Wang (Beihang University) · Yuelang Xu (Tsinghua University) · Kai Xu (National University of Defense Technology) · Andrea Tagliasacchi (Google Research, Brain) · Bin Zhou (State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering,Beihang University) · Ali Mahdavi-Amiri (Simon Fraser University) · Hao Zhang (Simon Fraser University)

基于核的加法神经网络渐进蒸馏
Kernel Based Progressive Distillation for Adder Neural Networks
Yixing Xu (Huawei Noah’s Ark Lab) · Yunhe Wang (Huawei Noah’s Ark Lab) · Xinghao Chen (Huawei Noah’s Ark Lab) · Wei Zhang (Noah’s Ark Lab, Huawei Inc.) · Chunjing XU (Huawei Technologies) · Chang Xu (University of Sydney)

跨模态音视频聚类的自我监督学习
Self-Supervised Learning by Cross-Modal Audio-Video Clustering
Humam Alwassel (KAUST) · Dhruv Mahajan (Facebook) · Bruno Korbar (Facebook) · Lorenzo Torresani (Facebook AI) · Bernard Ghanem (KAUST) · Du Tran (Facebook AI)

切片概率散度的统计和拓扑性质
Statistical and Topological Properties of Sliced Probability Divergences
Kimia Nadjahi (Télécom ParisTech) · Alain Durmus (ENS Paris Saclay) · Lénaïc Chizat (CNRS) · Soheil Kolouri (HRL Laboratories LLC) · Shahin Shahrampour (Texas A&M University) · Umut Simsekli (Institut Polytechnique de Paris/ University of Oxford)

UWSOD:迈向全监督级性能弱监督对象检测
UWSOD: Toward Fully-Supervised-Level Performance Weakly Supervised Object Detection
Yunhang Shen (Xiamen University) · Rongrong Ji (Xiamen University, China) · Zhiwei Chen (Xiamen University) · Yongjian Wu (Tencent Technology (Shanghai) Co.,Ltd) · Feiyue Huang (Tencent)

借助生成模型突破基于模型的强化学习中的样本量障碍
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
Gen Li (Tsinghua University) · Yuting Wei (Carnegie Mellon University) · Yuejie Chi (CMU) · Yuantao Gu (Tsinghua University) · Yuxin Chen (Princeton University)

任务稳健模型不可知元学习
Task-Robust Model-Agnostic Meta-Learning
Liam Collins (University of Texas at Austin) · Aryan Mokhtari (UT Austin) · Sanjay Shakkottai (University of Texas at Austin)

鞍点问题的高效无投影算法
Efficient Projection-free Algorithms for Saddle Point Problems
Cheng Chen (Shanghai Jiao Tong University) · Luo Luo (The Hong Kong University of Science and Technology) · Weinan Zhang (Shanghai Jiao Tong University) · Yong Yu (Shanghai Jiao Tong Unviersity)

学习多智能体协调以增强方向传感器网络中的目标覆盖率
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
Jing Xu (Peking University) · Fangwei Zhong (Peking University) · Yizhou Wang (Peking University)

虚假近邻的外推法及其收敛速度的提高
Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate
Akifumi Okuno (RIKEN AIP) · Hidetoshi Shimodaira (Kyoto University / RIKEN AIP)

多元集成神经网络
The Diversified Ensemble Neural Network
Shaofeng Zhang (University of Electronic Science and Technology of China) · Meng Liu ( university of electronic science and technology of china) · Junchi Yan (Shanghai Jiao Tong University)

在量化神经网络中搜索低位权重
Searching for Low-Bit Weights in Quantized Neural Networks
zhaohui yang (peking university) · Yunhe Wang (Huawei Noah’s Ark Lab) · Kai Han (Huawei Noah’s Ark Lab) · Chunjing XU (Huawei Technologies) · Chao Xu (Peking University) · Dacheng Tao (University of Sydney) · Chang Xu (University of Sydney)

特定于薄片的神经元特性可促进前馈网络中稳定,稳定的信号传播
Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks
Dongqi Han (Okinawa Institute of Science and Technology) · Erik De Schutter (OIST) · Sungho Hong (Okinawa Institute of Science and Technology)

深扩散不变Wasserstein分布分类
Deep Diffusion-Invariant Wasserstein Distributional Classification
Sung Woo Park+ (Chung-Ang University) · Dong Wook Shu (Chung-Ang Univ., Korea) · Junseok Kwon (Chung-Ang Univ., Korea)

具有非线性观测和生成先验的广义套索
The Generalized Lasso with Nonlinear Observations and Generative Priors
Zhaoqiang Liu (National University of Singapore) · Jonathan Scarlett (National University of Singapore)

学习学习变异语义记忆
Learning to Learn Variational Semantic Memory
Xiantong Zhen (University of Amsterdam) · Yingjun Du (University of Amsterdam) · Huan Xiong (Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)) · Qiang Qiu (Purdue University) · Cees Snoek (University of Amsterdam) · Ling Shao (Inception Institute of Artificial Intelligence)

一致的结构关系学习,实现零散度分割
Consistent Structural Relation Learning for Zero-Shot Segmentation
Peike Li (University of Technology Sydney) · Yunchao Wei (UTS) · Yi Yang (UTS)

通过屏障函数实现亚模最大化
Submodular Maximization Through Barrier Functions
Ashwinkumar Badanidiyuru (Google Research) · Amin Karbasi (Yale) · Ehsan Kazemi (Google) · Jan Vondrak (Stanford University)

使用生成对抗网络估算连续价值干预的效果
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks
Ioana Bica (University of Oxford) · James Jordon (University of Oxford) · Mihaela van der Schaar (University of Cambridge)

测试时间延长的学习损失
Learning Loss for Test-Time Augmentation
Ildoo Kim (Kakao Brain) · Younghoon Kim (Sungshin Women’s University) · Sungwoong Kim (Kakao Brain)

神经网络中简单性偏差的陷阱
The Pitfalls of Simplicity Bias in Neural Networks
Harshay Shah (Microsoft Research) · Kaustav Tamuly (Microsoft Research) · Aditi Raghunathan (Stanford University) · Prateek Jain (Microsoft Research) · Praneeth Netrapalli (Microsoft Research)

投影有效次梯度法和最优非光滑Frank-Wolfe
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe
Kiran Thekumparampil (Univ. of Illinois at Urbana-Champaign) · Prateek Jain (Microsoft Research) · Praneeth Netrapalli (Microsoft Research) · Sewoong Oh (University of Washington)

马尔可夫数据的最小二乘回归:基本极限和算法
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
Dheeraj Nagaraj (Massachusetts Institute of Technology) · Xian Wu (Stanford University) · Guy Bresler (MIT) · Prateek Jain (Microsoft Research) · Praneeth Netrapalli (Microsoft Research)

差分私有联合上下文强盗
Differentially-Private Federated Contextual Bandits
Abhimanyu Dubey (MIT) · Alex `Sandy’ Pentland (MIT)

观察到的非政策模仿学习
Off-Policy Imitation Learning from Observations
Zhuangdi Zhu (Michigan State University) · Kaixiang Lin (Michigan State University) · Bo Dai (Google Brain) · Jiayu Zhou (Michigan State University)

神经非刚性跟踪
Neural Non-Rigid Tracking
Aljaz Bozic (Technical University Munich) · Pablo Palafox (Technical University Munich) · Michael Zollhöfer (Stanford University) · Angela Dai (Technical University of Munich) · Justus Thies (Technical University of Munich) · Matthias Niessner (Technical University of Munich)

逐层递减加速基于变压器的语言模型的训练
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping
Minjia Zhang (Microsoft) · Yuxiong He (Microsoft)

深度维纳反卷积:维纳与深度学习相结合的图像去模糊
Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring
Jiangxin Dong (Max Planck Institute for Informatics) · Stefan Roth (TU Darmstadt) · Bernt Schiele (Max Planck Institute for Informatics)

AdaTune:自适应张量程序编译变得高效
AdaTune: Adaptive Tensor Program Compilation Made Efficient
Menghao Li (Microsoft) · Minjia Zhang (Microsoft) · Chi Wang (Microsoft Research) · Mingqin Li (Microsoft)

纹理插值以探测视觉感知
Texture Interpolation for Probing Visual Perception
Jonathan Vacher (Albert Einstein College of Medicine) · Aida Davila (Albert Einstein College of Medicine) · Adam Kohn (Albert Einstein College of Medicine) · Ruben Coen-Cagli (Albert Einstein College of Medicine)

稳定且富有表现力的经常性视觉模型
Stable and expressive recurrent vision models
Drew Linsley (Brown University) · Alekh Karkada Ashok (Brown University) · Lakshmi Narasimhan Govindarajan (Brown University) · Rex Liu (Brown University) · Thomas Serre (Brown University)

从时间序列深度重建奇怪的吸引子
Deep reconstruction of strange attractors from time series
William Gilpin (Harvard University)

SDF-SRN:从静态图像中学习签名距离3D对象重建
SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images
Chen-Hsuan Lin (Carnegie Mellon University) · Chaoyang Wang (Carnegie Mellon University) · Simon Lucey (CMU)

不断发展的图形规划师:视觉和语言导航的上下文全局规划
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation
Zhiwei Deng (Princeton University) · Karthik Narasimhan (Princeton University) · Olga Russakovsky (Princeton University)

通过图网络进行Q学习可以为SAT解算器学习可推广的分支启发法吗?
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?
Vitaly Kurin (University of Oxford) · Saad Godil (NVIDIA) · Shimon Whiteson (University of Oxford) · Bryan Catanzaro (NVIDIA)

可校准的对抗训练:免费在鲁棒性和准确性之间进行就地权衡
Calibratable Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
Haotao Wang (University of Texas at Austin) · Tianlong Chen (Unversity of Texas at Austin) · Shupeng Gui (University of Rochester) · TingKuei Hu (Texas A&M University) · Ji Liu (Kwai Inc.) · Zhangyang Wang (University of Texas at Austin)

数据并行SGD的自适应梯度量化
Adaptive Gradient Quantization for Data-Parallel SGD
Fartash Faghri (University of Toronto) · Iman Tabrizian (University of Toronto) · Ilia Markov (IST Austria) · Dan Alistarh (IST Austria & Neural Magic Inc.) · Daniel Roy (Univ of Toronto & Vector) · Ali Ramezani-Kebrya (Vector Institute)

用动态归一化流建模连续随机过程
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Ruizhi Deng (Simon Fraser University) · Bo Chang (Borealis AI) · Marcus Brubaker (Borealis AI) · Greg Mori (Borealis AI) · Andreas Lehrmann (Borealis AI)

具有周期激活函数的隐式神经表示
Implicit Neural Representations with Periodic Activation Functions
Vincent Sitzmann (Stanford University) · Julien Martel (Stanford University) · Alexander Bergman (Stanford University) · David Lindell (Stanford University) · Gordon Wetzstein (Stanford University)

基于耦合的可逆神经网络是通用微差同构近似器
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
Takeshi Teshima (The University of Tokyo) · Isao Ishikawa (Ehime University) · Koichi Tojo (RIKEN AIP) · Kenta Oono (The University of Tokyo, Preferred Networks Inc.) · Masahiro Ikeda (RIKEN AIP) · Masashi Sugiyama (RIKEN / University of Tokyo)

MetaSDF:元学习符号距离函数
MetaSDF: Meta-Learning Signed Distance Functions
Vincent Sitzmann (Stanford University) · Eric Chan (Stanford University) · Richard Tucker (Google) · Noah Snavely (Cornell University and Google AI) · Gordon Wetzstein (Stanford University)

具有Vertex Infomax池的图跨网络
Graph Cross Networks with Vertex Infomax Pooling
Maosen Li (Shanghai Jiao Tong University) · Siheng Chen (MERL) · Ya Zhang (Cooperative Medianet Innovation Center, Shang hai Jiao Tong University) · Ivor Tsang (University of Technology, Sydney)

对抗性与后门鲁棒性之间的权衡
On the Trade-off between Adversarial and Backdoor Robustness
Cheng-Hsin Weng (National Tsing Hua University) · Yan-Ting Lee (National Tsing Hua University) · Shan-Hung (Brandon) Wu (National Tsing Hua University)

零射击学习的密集特征组合
Dense Feature Composition for Zero-Shot Learning
Dat Huynh (Northeastern University) · Ehsan Elhamifar (Northeastern University)

用于多任务学习的梯度手术
Gradient Surgery for Multi-Task Learning
Tianhe Yu (Stanford University) · Saurabh Kumar (Stanford University) · Abhishek Gupta (UC Berkeley) · Sergey Levine (UC Berkeley) · Karol Hausman (Google Brain) · Chelsea Finn (Stanford)

联合主成分分析。
Federated Principal Component Analysis.
Andreas Grammenos (University of Cambridge) · Rodrigo Mendoza Smith (Quine Technologies) · Jon Crowcroft (University of Cambridge) · Cecilia Mascolo (University of Cambridge)

公平约束可以帮助在结构化预测中进行准确推断
Fairness constraints can help exact inference in structured prediction
Kevin Bello (Purdue University) · Jean Honorio (Purdue University)

矩阵透视函数的邻近算子及其应用
Proximity Operator of the Matrix Perspective Function and its Applications
Joong-Ho Won (Seoul National University)

通用工具变量模型的一类算法
A Class of Algorithms for General Instrumental Variable Models
Niki Kilbertus (Helmholtz AI) · Matt Kusner (University College London) · Ricardo Silva (University College London)

CodeCMR:功能级二进制源代码匹配的跨模态检索
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching
Zeping Yu (Tencent Security Keen Lab) · Wenxin Zheng (Shanghai JiaoTong University, Tencent KeenLab) · Jiaqi Wang (Tencent Keen Lab, Technical University of Munich) · Qiyi Tang (Tencent Keen Lab) · Sen Nie (Tencent Keen Lab) · Shi Wu (Tencent Keen Lab)

具有方差降低的双自由随机分散优化
Dual-Free Stochastic Decentralized Optimization with Variance Reduction
Hadrien Hendrikx (INRIA - PSL) · Francis Bach (INRIA - Ecole Normale Superieure) · Laurent Massoulié (Inria)

线性反向传播可提高对抗性示例的可传递性
Backpropagating Linearly Improves Transferability of Adversarial Examples
Yiwen Guo (ByteDance AI Lab) · Qizhang Li (ByteDance AI Lab) · Hao Chen (UC Davis)

适用于任意半规范的最近原型分类器的快速对抗鲁棒性认证
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms
Sascha Saralajew (Dr. Ing. h.c. F. Porsche AG) · Lars Holdijk (University of Amsterdam) · Thomas Villmann (University of Applied Sciences Mittweida)

具有随机平滑功能的黑匣子认证:基于功能优化的框架
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework
Dinghuai Zhang (Peking University) · Mao Ye (The University of Texas at Austin) · Chengyue Gong (Peking University) · Zhanxing Zhu (Peking University) · Qiang Liu (UT Austin)

SLOPE的严格筛选规则
The Strong Screening Rule for SLOPE
Johan Larsson (Lund University) · Malgorzata Bogdan (University of Wroclaw) · Jonas Wallin (Lund university)

推荐生态系统中的内容提供商动态和协调
Content Provider Dynamics and Coordination in Recommendation Ecosystems
Omer Ben-Porat (Technion – Israel Institute of Technology) · Itay Rosenberg (Technion) · Moshe Tennenholtz (Technion–Israel Institute of Technology)

磁盘:通过策略梯度学习本地功能
DISK: Learning local features with policy gradient
Michał Tyszkiewicz (EPFL) · Pascal Fua (EPFL, Switzerland) · Eduard Trulls (Google)

学习个人推理沟通以实现多智能体合作
Learning Individually Inferred Communication for Multi-Agent Cooperation
Ziluo Ding (Peking University) · Tiejun Huang (Peking University) · Zongqing Lu (Peking University)

析因政策的终生政策梯度学习,可以更快地进行培训而不会忘记
Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting
Jorge Mendez (University of Pennsylvania) · Boyu Wang (University of Western Ontario) · Eric Eaton (University of Pennsylvania)

硬负混合用于对比学习
Hard Negative Mixing for Contrastive Learning
Yannis Kalantidis (Facebook) · Mert B Sariyildiz (Bilkent University) · Noe Pion (NAVER Labs Europe) · Philippe Weinzaepfel (NAVER LABS Europe) · Diane Larlus (Naver Labs Europe)

稳健的量化:一种全部统治的模型
Robust Quantization: One Model to Rule Them All
Moran Shkolnik (Intel) · Brian Chmiel (Intel) · Ron Banner (Intel - Artificial Intelligence Products Group (AIPG)) · Gil Shomron (Technion - Israel Institute of Technology) · Yury Nahshan (Intel - Artificial Intelligence Products Group (AIPG)) · Alex Bronstein (Technion) · Uri Weiser (Technion - Israel Institute of Technology)

投影鲁棒Wasserstein距离和黎曼优化
Projection Robust Wasserstein Distance and Riemannian Optimization
Tianyi Lin (UC Berkeley) · Chenyou Fan (The Chinese University of Hong Kong, Shenzhen) · Nhat Ho (University of Texas at Austin) · Marco Cuturi (Google Brain & CREST - ENSAE) · Michael Jordan (UC Berkeley)

固定支持的Wasserstein重心:计算硬度和快速算法
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm
Tianyi Lin (UC Berkeley) · Nhat Ho (University of Texas at Austin) · Xi Chen (New York University) · Marco Cuturi (Google Brain & CREST - ENSAE) · Michael Jordan (UC Berkeley)

隐式秩最小自动编码器
Implicit Rank-Minimizing Autoencoder
Li Jing (Facebook AI Research) · Jure Zbontar (Facebook) · yann lecun (Facebook)

镜像朗格万扩散的指数遍历性
Exponential ergodicity of mirror-Langevin diffusions
Sinho Chewi (Massachusetts Institute of Technology) · Thibaut Le Gouic (Massachusetts Institute of Technology) · Chen Lu (Massachusetts Institute of Technology) · Tyler Maunu (Massachusetts Institute of Technology) · Philippe Rigollet (MIT) · Austin J Stromme (MIT)

深度强化和InfoMax学习
Deep Reinforcement and InfoMax Learning
Bogdan Mazoure (McGill University) · Remi Tachet des Combes (Microsoft Research Montreal) · Thang Long DOAN (McGill) · Philip Bachman (Microsoft Research) · R Devon Hjelm (Microsoft Research)

数据增强的群体理论框架
A Group-Theoretic Framework for Data Augmentation
Shuxiao Chen (University of Pennsylvania) · Edgar Dobriban (University of Pennsylvania) · Jane Lee (University of Pennsylvania)

重新参数化梯度的基于近似的方差减少
Approximation Based Variance Reduction for Reparameterization Gradients
Tomas Geffner (UMass Amherst) · Justin Domke (University of Massachusetts, Amherst)

开放图基准:图上机器学习的数据集
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu (Stanford University) · Matthias Fey (TU Dortmund University) · Marinka Zitnik (Harvard University) · Yuxiao Dong (Microsoft) · Hongyu Ren (Stanford University) · Bowen Liu (Stanford University) · Michele Catasta (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

在线快速适应和知识积累:一种持续学习的新方法
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Massimo Caccia (MILA) · Pau Rodriguez (CVC) · Oleksiy Ostapenko (University of Montreal, MILA) · Fabrice Normandin (MILA) · Min Lin (MILA) · Lucas Page-Caccia (McGill University) · Issam Hadj Laradji (University of British Columbia) · Irina Rish (Mila/UdeM) · Alexandre Lacoste (Element AI) · David Vázquez (Element AI) · Laurent Charlin (MILA / U.Montreal)

知识增强的深度神经网络,用于面部表情和动作单元识别
Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition
Zijun Cui (Rensselaer Polytechnic Institute) · Tengfei Song (Southeast University) · Yuru Wang (Northeast Normal University) · Qiang Ji (Rensselaer Polytechnic Institute)

从稀疏奖励中学习的基于记忆轨迹的策略
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards
Yijie Guo (University of Michigan) · Jongwook Choi (University of Michigan) · Marcin Moczulski (Google Brain) · Shengyu Feng (University of Illinois Urbana Champaign) · Samy Bengio (Google Research, Brain Team) · Mohammad Norouzi (Google Brain) · Honglak Lee (Google / U. Michigan)

确定性系统中具有函数逼近的不可知Q学习:逼近误差和样本复杂度的接近最优界
Agnostic Q-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity
Simon Du (Institute for Advanced Study) · Jason Lee (Princeton University) · Gaurav Mahajan (University of California, San Diego) · Ruosong Wang (Carnegie Mellon University)

交换自动编码器以进行深度图像处理
Swapping Autoencoder for Deep Image Manipulation
Taesung Park (UC Berkeley) · Jun-Yan Zhu (Adobe, CMU) · Oliver Wang (Adobe Research) · Jingwan Lu (Adobe Research) · Eli Shechtman (Adobe Research, US) · Alexei Efros (UC Berkeley) · Richard Zhang (Adobe)

ISTA-NAS:稀疏编码的高效一致的神经体系结构搜索
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
Yibo Yang (Peking University) · Hongyang Li (Peking University) · Shan You (SenseTime) · Fei Wang (SenseTime) · Chen Qian (SenseTime) · Zhouchen Lin (Peking University)

通过自我监督实现通用域适应
Universal Domain Adaptation through Self Supervision
Kuniaki Saito (Boston University) · Donghyun Kim (Boston University) · Stan Sclaroff (Boston University) · Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)

OOD-MAML:元学习的少量热销分布检测和分类
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification
Taewon Jeong (KAIST) · Heeyoung Kim (KAIST)

辅助任务权重最小数据学习
Auxiliary Task Reweighting for Minimum-data Learning
Baifeng Shi (Peking University) · Judy Hoffman (Georgia Institute of Technology) · Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research) · Trevor Darrell (UC Berkeley) · Huijuan Xu (University of California, Berkeley)

理论启发的路径规范化差分网络架构搜索
Theory-Inspired Path-Regularized Differential Network Architecture Search
Pan Zhou (Salesforce) · Caiming Xiong (Salesforce) · Richard Socher (Salesforce) · Steven Chu Hong Hoi (Salesforce)

通过学习解析表达式进行成分合成
Compositional Generalization by Learning Analytical Expressions
Qian Liu (Beihang University) · Shengnan An (Xi’an Jiaotong University) · Jian-Guang Lou (Microsoft) · Bei Chen (Microsoft Research Asia) · Zeqi Lin (Microsoft) · Yan Gao (Microsoft Research Asia, Beijing, China) · Bin Zhou (State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering,Beihang University) · Nanning Zheng (Xi’an Jiaotong University) · Dongmei Zhang (Microsoft Research)

通过对比群集分配进行视觉特征的无监督学习
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Mathilde Caron (INRIA / FAIR) · Ishan Misra (Facebook AI Research ) · Julien Mairal (Inria) · Priya Goyal (Facebook AI Research) · Piotr Bojanowski (Facebook) · Armand Joulin (Facebook AI research)

通过深度视频优先级实现盲时视频一致性
Blind Temporal Video Consistency via Deep Video Prior
Chenyang Lei (HKUST) · Yazhou Xing (HKUST) · Qifeng Chen (HKUST)

对偶T:减少标签噪声学习中转换矩阵的估计误差
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
Yu Yao (University of Sydney) · Tongliang Liu (The University of Sydney) · Bo Han (HKBU / RIKEN) · Mingming Gong (University of Melbourne) · Jiankang Deng (Imperial College London) · Gang Niu (RIKEN) · Masashi Sugiyama (RIKEN / University of Tokyo)

机器学习中自动微分的数学模型
A mathematical model for automatic differentiation in machine learning
Jérôme Bolte (Université Toulouse 1) · Edouard Pauwels (IRIT)

用于图像超分辨率的跨尺度内部图卷积网络
Cross-scale Internal Graph Convolution Network for Image Super-Resolution
Shangchen Zhou (Nanyang Technological University) · Jiawei Zhang (Sensetime Research) · Wangmeng Zuo (Harbin Institute of Technology) · Chen Change Loy (Nanyang Technological University)

无悔学习和纳什均衡混合:他们不混合
No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix
Emmanouil-Vasileios Vlatakis-Gkaragkounis (Columbia University) · Lampros Flokas (Columbia University) · Thanasis Lianeas (National Technical University of Athens) · Panayotis Mertikopoulos (CNRS (French National Center for Scientific Research)) · Georgios Piliouras (Singapore University of Technology and Design)

LoopReg:隐式曲面对应,姿势和形状的3D人体网格物体自监督学习
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration
Bharat Bhatnagar (MPI-INF) · Cristian Sminchisescu (Google Research) · Christian Theobalt (MPI Informatik) · Gerard Pons-Moll (MPII, Germany)

学习外推知识:传导性射偏图超链接预测
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
Jinheon Baek (KAIST) · Dong Bok Lee (KAIST) · Sung Ju Hwang (KAIST, AITRICS)

基于近最佳比较的聚类
Near-Optimal Comparison Based Clustering
Michaël Perrot (Max Planck Institute for Intelligent Systems) · Pascal Esser (Technical University of Munich) · Debarghya Ghoshdastidar (Technical University Munich)

主动不变因果预测:通过稳定性进行实验选择
Active Invariant Causal Prediction: Experiment Selection through Stability
Juan Gamella (ETH Zürich) · Christina Heinze-Deml (ETH Zurich)

隐函数学习的神经无符号距离场
Neural Unsigned Distance Fields for Implicit Function Learning
Julian Chibane (Max Planck Institute for Informatics, University of Wuerzburg) · Mohamad Aymen mir (MPI Informatics, Saarland) · Gerard Pons-Moll (MPII, Germany)

BrainProp:大脑如何实现基于奖励的错误反向传播
BrainProp: How the brain can implement reward-based error backpropagation
Isabella Pozzi (Centrum Wiskunde & Informatica) · Sander Bohte (CWI) · Pieter Roelfsema (Netherlands Institute for Neuroscience)

通过参考优势分解实现几乎最佳的无模型强化学习
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition
Zihan Zhang (Tsinghua University) · Yuan Zhou (UIUC) · Xiangyang Ji (Tsinghua University)

双曲空间中的鲁棒大余量学习
Robust large-margin learning in hyperbolic space
Melanie Weber (Princeton University) · Manzil Zaheer (Google Research) · Ankit Singh Rawat (Google Research) · Aditya Menon (Google) · Sanjiv Kumar (Google Research)

腐败的敌对强盗:遗憾的下界和无遗憾的算法
Adversarial Bandits with Corruptions: Regret Lower Bound and No-regret Algorithm
lin yang (UMass) · Mohammad Hajiesmaili (UMass Amherst) · Mohammad Sadegh Talebi (University of Copenhagen) · John C. S. Lui (The Chinese University of Hong Kong) · Wing Shing Wong (The Chinese University of Hong Kong)

分段多项式趋势的自适应在线估计
Adaptive Online Estimation of Piecewise Polynomial Trends
Dheeraj Baby (UC Santa Barbara) · Yu-Xiang Wang (UC Santa Barbara)

深度网络中领域不变学习的字典方法
A Dictionary Approach to Domain-Invariant Learning in Deep Networks
Ze Wang (Duke University) · Xiuyuan Cheng (Duke University) · Guillermo Sapiro (Duke University) · Qiang Qiu (Purdue University)

零件相关的标签噪声:朝着实例相关的标签噪声
Parts-dependent Label Noise: Towards Instance-dependent Label Noise
Xiaobo Xia (The University of Sydney / Xidian University) · Tongliang Liu (The University of Sydney) · Bo Han (HKBU / RIKEN) · Nannan Wang (Xidian University) · Mingming Gong (University of Melbourne) · Haifeng Liu (Brain-Inspired Technology Co., Ltd.) · Gang Niu (RIKEN) · Dacheng Tao (University of Sydney) · Masashi Sugiyama (RIKEN / University of Tokyo)

域自适应中具有较低偏差和方差的可转移校准
Transferable Calibration with Lower Bias and Variance in Domain Adaptation
Ximei Wang (Tsinghua University) · Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University) · Michael Jordan (UC Berkeley)

TSPNet:通过时间语义金字塔进行手语翻译的分层特征学习
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation
DONGXU LI (THE AUSTRALIAN NATIONAL UNIVERSITY) · Chenchen Xu (The Australian National University) · Xin Yu (University of Technology Sydney) · Kaihao Zhang (Australian National University) · Benjamin Swift (Australian National University) · Hanna Suominen (The Australian National University and Data61/CSIRO) · Hongdong Li (Australian National University)

带有改组的SGD:最佳速率,无组件凸度和历时要求
SGD with shuffling: optimal rates without component convexity and large epoch requirements
Kwangjun Ahn (MIT) · Chulhee Yun (MIT) · Suvrit Sra (MIT)

马尔可夫链共现矩阵的浓度界
Concentration Bounds for Co-occurrence Matrices of Markov Chains
Jiezhong Qiu (Tsinghua University) · Chi Wang (Microsoft Research) · Ben Liao (Tencent) · Richard Peng (Georgia Tech) · Jie Tang (Tsinghua University)

一种基于可伸缩的基于MIP的最佳多元决策树学习方法
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
Haoran Zhu (University of Wisconsin-Madison) · Pavankumar Murali (IBM) · Dzung Phan (IBM Research, T. J. Watson Research Center) · Lam Nguyen (IBM Research, Thomas J. Watson Research Center) · Jayant Kalagnanam (IBM Research)

Restless-UCB,一种针对在线躁狂匪徒的高效且低复杂度的算法
Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits
Siwei Wang (IIIS, Tsinghua University) · Longbo Huang (IIIS, Tsinghua Univeristy) · John C. S. Lui (The Chinese University of Hong Kong)

元社区
Meta-Neighborhoods
Siyuan Shan (UNC Chapel Hill) · Yang Li (UNC-Chapel Hill) · Junier Oliva (UNC - Chapel Hill)

ICNet:用于共显着性检测的显着内部相关网络
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
Wen-Da Jin (Tianjin University) · Jun Xu (Nankai University) · Ming-Ming Cheng (Nankai University) · Yi Zhang (Tianjin University) · Wei Guo (Tianjin University)

一致的部分标签学习
Provably Consistent Partial-Label Learning
Lei Feng (Nanyang Technological University) · Jiaqi Lv (Southeast University) · Bo Han (HKBU / RIKEN) · Miao Xu (RIKEN AIP) · Gang Niu (RIKEN) · Xin Geng (Southeast University) · Bo An (Nanyang Technological University) · Masashi Sugiyama (RIKEN / University of Tokyo)

学会利用奖励塑造:奖励塑造的新方法
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
Yujing Hu (NetEase Fuxi AI Lab) · Weixun Wang (Tianjin University) · Hangtian Jia (Netease Fuxi AI Lab) · Yixiang Wang (University of Science and Technology of China) · Yingfeng Chen (NetEase Fuxi AI Lab) · Jianye Hao (Tianjin University) · Feng Wu (University of Science and Technology of China) · Changjie Fan (NetEase Fuxi AI Lab)

分层量化自动编码器
Hierarchical Quantized Autoencoders
Will Williams (Speechmatics) · Sam Ringer (Speechmatics) · Tom Ash (Speechmatics) · David MacLeod (Speechmatics) · Jamie Dougherty (Speechmatics) · John Hughes (Speechmatics)

成对学习的更清晰泛化界线
Sharper Generalization Bounds for Pairwise Learning
Yunwen Lei (University of Birmingham) · Antoine Ledent (TU Kaiserslautern) · Marius Kloft (TU Kaiserslautern)

通过适配器将BERT合并到并行序列解码中
Incorporating BERT into Parallel Sequence Decoding with Adapters
Junliang Guo (University of Science and Technology of China) · Zhirui Zhang (Alibaba Group Inc.) · Linli Xu (University of Science and Technology China) · Hao-Ran Wei (Alibaba DAMO Academy) · Boxing Chen (Alibaba Group) · Enhong Chen (University of Science and Technology of China)

半监督学习的无监督语义聚合和可变形模板匹配
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
Qi Wang (Northwestern Polytechnical University) · Tao Han (Northwestern Polytechnical University) · Junyu Gao (Northwestern Polytechnical University, Center for OPTical IMagery Analysis and Learning) · Yuan Yuan (Northwestern Polytechnical University)

ICE-BeeM:基于非线性ICA的可识别的基于条件能量的深度模型
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
Ilyes Khemakhem (UCL) · Ricardo Monti (UCL) · Diederik P. Kingma (Google) · Aapo Hyvarinen (University of Helsinki)

基于人口的强化学习中的有效多样性
Effective Diversity in Population Based Reinforcement Learning
Jack Parker-Holder (University of Oxford) · Aldo Pacchiano (UC Berkeley) · Krzysztof M Choromanski (Google Brain Robotics) · Stephen J Roberts (University of Oxford)

相关文章使用结构化潜在空间的物理上可行的多对象场景合成
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
Sebastien Ehrhardt (University of Oxford) · Oliver Groth (Oxford Robotics Institute) · Aron Monszpart (Niantic) · Martin Engelcke (University of Oxford) · Ingmar Posner (Oxford University) · Niloy Mitra (UCL/Adobe) · Andrea Vedaldi (Facebook AI Research and University of Oxford)

基于人口的匪徒行之有效的在线超参数优化
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits
Jack Parker-Holder (University of Oxford) · Vu Nguyen (University of Oxford) · Stephen J Roberts (University of Oxford)

随机归一化
Stochastic Normalization
Zhi Kou (Tsinghua University) · Kaichao You (Tsinghua University) · Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University)

增强学习的布尔任务代数
A Boolean Task Algebra for Reinforcement Learning
Geraud Nangue Tasse (University of the Witwatersrand) · Steven James (University of the Witwatersrand) · Benjamin Rosman (University of the Witwatersrand / CSIR)

一致的多标签分类网络
Consistent Hierarchical Multi-Label Classification Networks
Eleonora Giunchiglia (University of Oxford) · Thomas Lukasiewicz (University of Oxford)

基于条件互信息的通用化界线及其在有噪迭代算法中的应用
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms
Mahdi Haghifam (University of Toronto) · Jeffrey Negrea (University of Toronto) · Ashish Khisti (University of Toronto) · Daniel Roy (Univ of Toronto & Vector) · Gintare Karolina Dziugaite (Element AI)

具有线性函数逼近的Q学习的新收敛变体
A new convergent variant of Q-learning with linear function approximation
Diogo Carvalho (GAIPS, INESC-ID) · Francisco S. Melo (IST/INESC-ID) · Pedro A. Santos (Instituto Superior Técnico)

学习动态环境的纠缠表示和组结构
Learning Disentangled Representations and Group Structure of Dynamical Environments
Robin Quessard (indust.ai) · Thomas Barrett (University of Oxford) · William Clements (indust.ai)

寻找强有力的概括方法
In search of robust measures of generalization
Gintare Karolina Dziugaite (Element AI) · Alexandre Drouin (Element AI) · Brady Neal (Mila) · Nitarshan Rajkumar (Mila, Université de Montréal) · Ethan Caballero (Mila) · Linbo Wang (University of Toronto) · Ioannis Mitliagkas (Mila & University of Montreal) · Daniel Roy (Univ of Toronto & Vector)

连续正则Wasserstein重心
Continuous Regularized Wasserstein Barycenters
Lingxiao Li (MIT) · Aude Genevay (MIT) · Mikhail Yurochkin (IBM Research, MIT-IBM Watson AI Lab) · Justin M Solomon (MIT)

联合多图匹配和聚类的分级分配及其在无监督图匹配网络学习中的应用
Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning
Runzhong Wang (Shanghai Jiao Tong University) · Junchi Yan (Shanghai Jiao Tong University) · Xiaokang Yang (Shanghai Jiao Tong University)

在复杂环境中避免副作用
Avoiding Side Effects in Complex Environments
Alex Turner (Oregon State University) · Neale Ratzlaff (Oregon State University) · Prasad Tadepalli (Oregon State University)

对抗性体重扰动可提高对抗性训练
Adversarial Weight Perturbation Improves Adversarial Training
Dongxian Wu (Tsinghua University) · Yisen Wang (Peking University) · Shu-Tao Xia (Tsinghua University)

自我监督的生成对抗性压缩
Self-Supervised Generative Adversarial Compression
Chong Yu (NVIDIA) · Chong Yu (Intel)

Permute-and-Flip:差异化私人选择的新机制
Permute-and-Flip: A new mechanism for differentially-private selection
Ryan McKenna (University of Massachusetts, Amherst) · Daniel Sheldon (University of Massachusetts Amherst)

SE(3)-变形金刚:3D旋转平移等距注意力网络
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
Fabian Fuchs (University of Oxford) · Daniel Worrall (University of Amsterdam) · Volker Fischer (Robert Bosch GmbH, Bosch Center for Artificial Intelligence) · Max Welling (University of Amsterdam / Qualcomm AI Research)

自适应降阶回归
Adaptive Reduced Rank Regression
Qiong Wu (College of William and Mary) · Felix MF Wong (Google) · Yanhua Li (“Worcester Polytechnic Institute, USA”) · Zhenming Liu (William and Mary) · Varun Kanade (University of Oxford)

多任务深度强化学习中的知识传递,以实现持续控制
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control
Zhiyuan Xu (Syracuse University) · Kun Wu (Syracuse University) · Zhengping Che (DiDi AI Labs, Didi Chuxing) · Jian Tang (DiDi AI Labs, DiDi Chuxing) · Jieping Ye (Didi Chuxing)

学习可变形四面体网格以进行3D重建
Learning Deformable Tetrahedral Meshes for 3D Reconstruction
Jun Gao (University of Toronto) · Wenzheng Chen (University of Toronto) · Tommy Xiang (University of Toronto) · Alec Jacobson (University of Toronto) · Morgan McGuire (NVIDIA) · Sanja Fidler (University of Toronto)

校准CNN以进行终身学习
Calibrating CNNs for Lifelong Learning
Pravendra Singh (Indian Institute of Technology Kanpur) · Vinay Kumar Verma (Indian Institute of Technology Kanpur) · Pratik Mazumder (Indian Institute of Technology, Kanpur) · Lawrence Carin (Duke University) · Piyush Rai (IIT Kanpur)

广义震源损失:学习用于密集物体检测的合格和分布式边界框
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Xiang Li (NJUST) · Wenhai Wang (Nanjing University) · Lijun Wu (Sun Yat-sen University) · Shuo Chen (Nanjing University of Science and Technology) · Xiaolin Hu (Tsinghua University) · Jun Li (Nanjing University of Science and Technology) · Jinhui Tang (Nanjing University of Science and Technology) · Jian Yang (Nanjing University of Science and Technology)

加权图中可证明的重叠社区检测
Provable Overlapping Community Detection in Weighted Graphs
Jimit Majmudar (University of Waterloo) · Stephen Vavasis (University of Waterloo )

不会忘记的GAN记忆
GAN Memory with No Forgetting
Yulai Cong (Duke University) · Miaoyun Zhao (Duke University) · Jianqiao Li (Duke University) · Sijia Wang (Duke University) · Lawrence Carin (Duke University)

通过可转移的优先级和查询反馈来学习黑匣子攻击者
Learning Black-Box Attackers with Transferable Priors and Query Feedback
Jiancheng YANG (Shanghai Jiao Tong University) · Yangzhou Jiang (Shanghai Jiaotong University) · Xiaoyang Huang (Shanghai Jiao Tong University) · Bingbing Ni (Shanghai Jiao Tong University) · Chenglong Zhao (Shanghai Jiao Tong University)

学习分支的混合模型
Hybrid Models for Learning to Branch
Prateek Gupta (University of Oxford) · Maxime Gasse (Polytechnique Montréal) · Elias Khalil (University of Toronto) · Pawan K Mudigonda (University of Oxford) · Andrea Lodi (École Polytechnique Montréal) · Yoshua Bengio (Mila / U. Montreal)

对抗式自我监督的对比学习
Adversarial Self-Supervised Contrastive Learning
Minseon Kim (KAIST) · Jihoon Tack (KAIST) · Sung Ju Hwang (KAIST, AITRICS)

在线控制中的预测能力
The Power of Predictions in Online Control
Chenkai Yu (Tsinghua University) · Guanya Shi (Caltech) · Soon-Jo Chung (Caltech) · Yisong Yue (Caltech) · Adam Wierman (California Institute of Technology)

多任务批量强化学习与度量学习
Multi-task Batch Reinforcement Learning with Metric Learning
Jiachen Li (University of California, San Diego) · Quan Vuong (University of California San Diego) · Shuang Liu (University of California, San Diego) · Minghua Liu (UCSD) · Kamil Ciosek (Microsoft) · Henrik Christensen (UC San Diego) · Hao Su (UCSD)

通过双层优化的核心集,用于持续学习和流式传输
Coresets via Bilevel Optimization for Continual Learning and Streaming
Zalán Borsos (ETH Zurich) · Mojmir Mutny (ETH Zurich) · Andreas Krause (ETH Zurich)

自适应培训:超越经验风险最小化
Self-Adaptive Training: beyond Empirical Risk Minimization
Lang Huang (Peking University) · Chao Zhang (Peking University) · Hongyang Zhang (TTIC)

具有线性相关性正则化的医学影像分类的领域概括
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization
Haoliang Li (Nanyang Technological University) · Yufei Wang (Nanyang Technological University) · Renjie Wan (Nanyang Technological University) · Shiqi Wang (CityU) · Tie-Qiang Li (Karolinska Institute) · Alex Kot (Nanyang Technological University)

用于深度模型保护的护照感知归一化
Passport-aware Normalization for Deep Model Protection
Jie Zhang (University of Science and Technology of China) · Dongdong Chen (Microsoft Cloud AI) · Jing Liao (City University of Hong Kong) · Weiming Zhang (University of Science and Technology of China) · Gang Hua (Wormpex AI Research) · Nenghai Yu (University of Science and Technology of China)

基于位置的比例梯度用于模型量化和稀疏训练
Position-based Scaled Gradient for Model Quantization and Sparse Training
Jangho Kim (Seoul National University) · KiYoon Yoo (Seoul National University) · Nojun Kwak (Seoul National University)

GPS-Net:基于图的光度立体网络
GPS-Net: Graph-based Photometric Stereo Network
Zhuokun Yao (Tianjin University) · Kun Li (Tianjin University) · Ying Fu (Beijing Institute of Technology) · Haofeng Hu (Tianjin University) · Boxin Shi (Peking University)

作物的精华:一站式神经体系结构搜索的优先路径提取
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search
Houwen Peng (Microsoft Research) · Hao Du (Microsoft Research) · Hongyuan Yu (MSRA) · QI LI (Tsinghua Univeristy) · Jing Liao (City University of Hong Kong) · Jianlong Fu (Microsoft Research)

直接反馈调整范围适用于现代深度学习任务和体系结构
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures
Julien Launay (LightOn) · Iacopo Poli (LightOn) · François Boniface (LightOn) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL)

积极探索,保守更新:可变步长缩放的随机超梯度方法
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
Yu-Guan Hsieh (Université Grenoble Alpes / École Normale Supérieure Paris) · Franck Iutzeler (Univ. Grenoble Alpes) · Jérôme Malick (CNRS and LJK) · Panayotis Mertikopoulos (CNRS (French National Center for Scientific Research))

小批量混合线性回归的鲁棒元学习
Robust Meta-learning for Mixed Linear Regression with Small Batches
Weihao Kong (Stanford University) · Raghav Somani (University of Washington) · Sham Kakade (University of Washington & Microsoft Research) · Sewoong Oh (University of Washington)

使用蒙特卡洛方法的深度主动推理代理
Deep active inference agents using Monte-Carlo methods
Zafeirios Fountas (University College London; Emotech Labs) · Noor Sajid (University College London) · Pedro Mediano (University of Cambridge ) · Karl Friston (University College London)

ICAM:通过解缠表示和特征归因映射可解释的分类
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Cher Bass (King’s College London) · Mariana da Silva (King’s College London) · Carole Sudre (King’s College London) · Petru-Daniel Tudosiu (King’s College London) · Stephen Smith (FMRIB Centre - University of Oxford) · Emma Robinson (King’s College)

审核差异化私有机器学习:私有SGD有多私有?
Auditing Differentially Private Machine Learning: How Private is Private SGD?
Matthew Jagielski (Northeastern University) · Jonathan Ullman (Northeastern University) · Alina Oprea (Northeastern University)

双重解决方案通讯网络
Dual-Resolution Correspondence Networks
Xinghui Li (University of Oxford) · Kai Han (University of Oxford) · Shuda Li (University of Oxford) · Victor Prisacariu (University of Oxford)

ReLU网络的Lipschitz常数的半代数优化
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks
Tong Chen (LAAS-CNRS) · Jean B Lasserre (lasserre@laas.fr) · Victor Magron (LAAS-CNRS) · Edouard Pauwels (IRIT)

对抗训练是依赖数据的操作员规范正则化的一种形式
Adversarial Training is a Form of Data-dependent Operator Norm Regularization
Kevin Roth (ETH Zurich) · Yannic Kilcher (ETH Zurich) · Thomas Hofmann (ETH Zurich)

超越准确性:通过测量错误一致性来量化CNN和人类的逐次试验行为
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
Robert Geirhos (University of Tübingen) · Kristof Meding (University of Tübingen & MPI for Intelligent Systems) · Felix A. Wichmann (University of Tübingen)

具有生物物理约束的系统识别:内视网膜的电路模型
System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina
Cornelius Schröder (University of Tübingen) · David Klindt (University of Tübingen) · Sarah Strauss (University of Tübingen) · Katrin Franke (University of Tübingen) · Matthias Bethge (University of Tübingen) · Thomas Euler (University of Tübingen) · Philipp Berens (University of Tübingen)

揭秘正交蒙特卡洛及其他
Demystifying Orthogonal Monte Carlo and Beyond
Han Lin (Columbia University) · Haoxian Chen (Columbia University) · Krzysztof M Choromanski (Google Brain Robotics) · Tianyi Zhang (Columbia University) · Clement Laroche (Columbia University)

HyNet:具有混合相似性度量的本地描述符
HyNet: Local Descriptor with Hybrid Similarity Measure
Yurun Tian (Imperial College London) · Axel Barroso Laguna (Imperial College London) · Tony Ng (Imperial College London) · Vassileios Balntas (Scape Technologies) · Krystian Mikolajczyk (Imperial College London)

通过MultiView ICA对神经成像研究中的共享反应进行建模
Modeling Shared responses in Neuroimaging Studies through MultiView ICA
Hugo Richard (INRIA) · Luigi Gresele (MPI for Intelligent Systems, Tübingen) · Aapo Hyvarinen (University of Helsinki) · Bertrand Thirion (INRIA) · Alexandre Gramfort (INRIA) · Pierre Ablin (INRIA)

深度学习的功能重要性排名
Feature Importance Ranking for Deep Learning
Maksymilian Wojtas (University of Manchester) · Ke Chen (The University of Manchester)

广义线性模型的极大极界
Minimax Bounds for Generalized Linear Models
Kuan-Yun Lee (University of California, Berkeley) · Thomas Courtade (UC Berkeley)

通过分布和特征层次了解深度可逆网络的异常检测
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
Robin T Schirrmeister (University Medical Center Freiburg) · Yuxuan Zhou (Stuttgart University) · Tonio Ball (Albert-Ludwigs-University) · Dan Zhang (Bosch Center for Artificial Intelligence)

深度学习的图相似度
A graph similarity for deep learning
Seongmin Ok (Samsung Advanced Institute of Technology)

机器学习的概率线性求解器
Probabilistic Linear Solvers for Machine Learning
Jonathan Wenger (University of Tübingen) · Philipp Hennig (University of Tübingen and MPI for Intelligent Systems Tübingen)

不可知学习高斯边际下的半空间和ReLU的近似最佳SQ下界
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals
Ilias Diakonikolas (UW Madison) · Daniel Kane (UCSD) · Nikos Zarifis (University of Wisconsin-Madison)

FixMatch:通过一致性和信心简化半监督学习
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn (Google) · David Berthelot (Google Brain) · Nicholas Carlini (Google) · Zizhao Zhang (Google) · Han Zhang (Google) · Colin A Raffel (Google Brain) · Ekin Dogus Cubuk (Google Brain) · Alexey Kurakin (Google Brain) · Chun-Liang Li (Google)

MetaPerturb:适用于异构任务和体系结构的可转移正则器
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
Jeong Un Ryu (KAIST) · JaeWoong Shin (KAIST) · Hae Beom Lee (KAIST) · Sung Ju Hwang (KAIST, AITRICS)

Noise2Same:针对图像去噪优化自监控范围
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
Yaochen Xie (Texas A&M University) · Zhengyang Wang (Texas A&M University) · Shuiwang Ji (Texas A&M University)

随机方差减少加速对偶平均以进行有限和优化
Stochastic Variance Reduced Accelerated Dual Averaging for Finite-Sum Optimization
Chaobing Song (Tsinghua University) · Yong Jiang (Tsinghua) · Yi Ma (UC Berkeley)

权重相关性如何影响深度神经网络的泛化能力?
How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks?
Gaojie Jin (University of Liverpool) · Xinping Yi (University of Liverpool) · Liang Zhang (Institute of Software, Chinese Academy of Sciences) · Lijun Zhang (Institute of Software, Chinese Academy of Sciences) · Sven Schewe (University of Liverpool) · Xiaowei Huang (Liverpool University)

测量图像分类中自然分布变化的稳健性
Measuring Robustness to Natural Distribution Shifts in Image Classification
Rohan Taori (University of California, Berkeley) · Achal Dave (Carnegie Mellon University) · Vaishaal Shankar (UC Berkeley) · Nicholas Carlini (Google) · Benjamin Recht (UC Berkeley) · Ludwig Schmidt (UC Berkeley)

同时进行流形学习和密度估计的流程
Flows for simultaneous manifold learning and density estimation
Johann Brehmer (New York University) · Kyle Cranmer (New York University)

时空多任务套索的时空MEG / EEG源成像的统计控制
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso
Jerome-Alexis Chevalier (Inria Saclay Île-de-France) · Joseph Salmon (Université de Montpellier) · Alexandre Gramfort (INRIA) · Bertrand Thirion (INRIA)

鲁棒对抗增强学习的稳定性和收敛性:以线性二次系统为例
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems
Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Bin Hu (University of Illinois at Urbana-Champaign) · Tamer Basar (University of Illinois at Urbana-Champaign)

生成模型的鲁棒压缩感知
Robust compressed sensing of generative models
Ajil Jalal (University of Texas at Austin) · Liu Liu (University of Texas at Austin) · Alexandros Dimakis (University of Texas, Austin) · Constantine Caramanis (UT Austin)

通过神经正切核进行贝叶斯深度合奏
Bayesian Deep Ensembles via the Neural Tangent Kernel
Bobby He (University of Oxford) · Balaji Lakshminarayanan (Google Brain) · Yee Whye Teh (University of Oxford, DeepMind)

通过观看YouTube视频进行语义视觉导航
Semantic Visual Navigation by Watching YouTube Videos
Matthew Chang (UIUC) · Arjun Gupta (University of Illinois at Urbana-Champaign) · Saurabh Gupta (UIUC)

随机傅里叶特征的随机矩阵分析:超出高斯核,精确的相变和相应的双下降
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent
Zhenyu Liao (University of California, Berkeley) · Romain Couillet (Université Grenoble Alpes) · Michael W Mahoney (UC Berkeley)

弱视点检测中的互补注意自蒸馏
Complementary Attention Self-Distillation for Weakly-Supervised Object Detection
Zeyi Huang (carnegie mellon university) · Yang Zou (Carnegie Mellon University) · B. V. K. Vijaya Kumar (CMU, USA) · Dong Huang (Carnegie Mellon University)

跨场景3D人体姿势估计的推理阶段优化
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation
Jianfeng Zhang (NUS) · Xuecheng Nie (NUS) · Jiashi Feng (National University of Singapore)

受噪声保护的组的公平性的鲁棒优化
Robust Optimization for Fairness with Noisy Protected Groups
Serena Wang (Google) · Wenshuo Guo (UC Berkeley) · Harikrishna Narasimhan (Google Research) · Andrew Cotter (Google) · Maya Gupta (Google) · Michael Jordan (UC Berkeley)

IPM的分布稳健性以及与正则化和GAN的链接
Distributional Robustness with IPMs and links to Regularization and GANs
Hisham Husain (The Australian National University & Data61)

GPU加速的原始学习,可实现超快速的大规模分类
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification
John Halloran (University of California, Davis) · David M Rocke (University of California, Davis)

一样本制导对象表示分解
One-sample Guided Object Representation Disassembling
Zunlei Feng (Zhejiang University) · Yongming He (Zhejiang University) · Xinchao Wang (Stevens Institute of Technology) · Xin Gao (Alibaba Group) · Jie Lei (Zhejiang University) · Cheng Jin (Fudan University) · Mingli Song (Zhejiang University)

通过神经符号堆栈机进行组合泛化
Compositional Generalization via Neural-Symbolic Stack Machines
Xinyun Chen (UC Berkeley) · Chen Liang (Google Brain) · Adams Wei Yu (Google Brain) · Dawn Song (UC Berkeley) · Denny Zhou (Google Brain)

逐点相关性估计的神经方法
Neural Methods for Point-wise Dependency Estimation
Yao-Hung Hubert Tsai (Carnegie Mellon University) · Han Zhao (Carnegie Mellon University) · Makoto Yamada (Kyoto University/RIKEN AIP) · Louis-Philippe Morency (Carnegie Mellon University) · Russ Salakhutdinov (Carnegie Mellon University)

使用Gumbel-CRF的潜在模板归纳
Latent Template Induction with Gumbel-CRFs
Yao Fu (Columbia University) · Chuanqi Tan (Alibaba Group) · Bin Bi (Alibaba Group) · Mosha Chen (Alibaba Group) · Yansong Feng (Peking University) · Alexander Rush (Cornell University)

通过深度强化学习走向玩完整的MOBA游戏
Towards Playing Full MOBA Games with Deep Reinforcement Learning
Deheng Ye (Tencent) · Guibin Chen (Tencent) · Wen Zhang (Tencent) · chen sheng (qq) · Bo Yuan (Tencent) · Bo Liu (Tencent) · Jia Chen (Tencent) · Hongsheng Yu (Tencent) · Zhao Liu (Tencent) · Fuhao Qiu (Tencent AI Lab) · Liang Wang (Tencent) · Tengfei Shi (Tencent) · Yinyuting Yin (Tencent) · Bei Shi (Tencent AI Lab) · Lanxiao Huang (Tencent) · qiang fu (Tencent AI Lab) · Wei Yang (Tencent AI Lab) · Wei Liu (Tencent AI Lab)

DFIS:动态和快速实例细分
DFIS: Dynamic and Fast Instance Segmentation
Xinlong Wang (University of Adelaide) · Rufeng Zhang (Tongji University) · Tao Kong (Bytedance) · Lei Li (ByteDance AI Lab) · Chunhua Shen (University of Adelaide)

最佳排名的高效在线学习:通过梯度下降降低维度
Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent
Dimitris Fotakis (National Technical University of Athens) · Thanasis Lianeas (National Technical University of Athens) · Georgios Piliouras (Singapore University of Technology and Design) · Stratis Skoulakis (Singapore University of Technology and Design)

模仿政策和环境的错误界限
Error Bounds of Imitating Policies and Environments
Tian Xu (Nanjing University) · Ziniu Li (Nanjing University) · Yang Yu (Nanjing University)

通过学习生成定理来学习证明定理
Learning to Prove Theorems by Learning to Generate Theorems
Mingzhe Wang (Pinceton University) · Jia Deng (Princeton University)

这种互动对我有何影响?功能交互的可解释归因
How does this interaction affect me? Interpretable attribution for feature interactions
Michael Tsang (University of Southern California) · Sirisha Rambhatla (University of Southern California) · Yan Liu (University of Southern California)

从具有未知目标的软干预中发现因果:表征和学习
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
Amin Jaber (Purdue University) · Murat Kocaoglu (IBM Research) · Karthikeyan Shanmugam (IBM Research, NY) · Elias Bareinboim (Columbia University)

重新思考可学习的树过滤器以进行通用特征变换
Rethinking Learnable Tree Filter for Generic Feature Transform
Lin Song (Xi’an Jiaotong University) · Yanwei Li (The Chinese University of Hong Kong) · Zhengkai Jiang (Institute of Automation,Chinese Academy of Sciences) · Zeming Li (Megvii(Face++) Inc) · Xiangyu Zhang (MEGVII Technology) · Hongbin Sun (Xi’an Jiaotong University) · Jian Sun (Megvii, Face++) · Nanning Zheng (Xi’an Jiaotong University)

BERT失去耐心:通过提早退出就可以快速,可靠地推断
BERT Loses Patience: Fast and Robust Inference with Early Exit
Wangchunshu Zhou (Beihang University) · Canwen Xu (UC San Diego) · Tao Ge (Microsoft Research Asia) · Julian McAuley (UCSD) · Ke Xu (Beihang University) · Furu Wei (Microsoft Research Asia)

决策,反事实解释和战略行为
Decisions, Counterfactual Explanations and Strategic Behavior
Stratis Tsirtsis (MPI-SWS) · Manuel Gomez Rodriguez (Max Planck Institute for Software Systems)

合成,执行和调试:学习修复神经程序综合
Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis
Kavi Gupta (UC Berkeley) · Xinyun Chen (UC Berkeley) · Peter Ebert Christensen (Technical University of Denmark) · Dawn Song (UC Berkeley)

深度网络的训练后迭代层次数据增强
Post-training Iterative Hierarchical Data Augmentation for Deep Networks
Adil Khan (Innopolis University) · Khadija Fraz (Hazara University)

随机分割网络:建模空间相关的运动不确定性
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
Miguel Monteiro (Imperial College London) · Loic Le Folgoc (Imperial College London) · Daniel Coelho de Castro (Imperial College London) · Nick Pawlowski (Imperial College London) · Bernardo Marques (Imperial College London) · Konstantinos Kamnitsas (Imperial College London) · Mark van der Wilk (Imperial College) · Ben Glocker (Imperial College London)

MeshSDF:可区分的等值面提取
MeshSDF: Differentiable Iso-Surface Extraction
Edoardo Remelli (EPFL) · Artem Lukoyanov (EPFL) · Stephan Richter (Intel Labs) · Benoit Guillard (EPFL) · Timur Bagautdinov (Facebook) · Pierre Baque (Neural Concept SA) · Pascal Fua (EPFL, Switzerland)

CogLTX:将BERT应用于长文本
CogLTX: Applying BERT to Long Texts
Ming Ding (Tsinghua University) · Chang Zhou (Alibaba Group) · Hongxia Yang (Alibaba Group) · Jie Tang (Tsinghua University)

零射击学习的属性原型网络
Attribute Prototype Network for Zero-Shot Learning
Wenjia Xu (University of Chinese Academy of Sciences) · Yongqin Xian (Max Planck Institute Informatics) · Jiuniu Wang (City University of Hong Kong) · Bernt Schiele (Max Planck Institute for Informatics) · Zeynep Akata (University of Tübingen)

魔方模型:TinyNets的扭曲分辨率,深度和宽度
Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets
Kai Han (Huawei Noah’s Ark Lab) · Yunhe Wang (Huawei Noah’s Ark Lab) · Qiulin Zhang (Beijing University of Posts and Telecommunications) · Wei Zhang (Noah’s Ark Lab, Huawei Inc.) · Chunjing XU (Huawei Technologies) · Tong Zhang (Tencent AI Lab)

SIRI:用于空间描述解析的空间关系诱导网络
SIRI: Spatial Relation Induced Network For Spatial Description Resolution
peiyao wang (ShanghaiTech University) · Weixin Luo (Shanghaitech University) · Yanyu Xu (Shanghaitech University) · Haojie Li (Dalian University of Technology) · Shugong Xu (Shanghai University) · Jianyu Yang (Soochow University) · Shenghua Gao (Shanghaitech University)

模型不可知的多级解释
Model Agnostic Multilevel Explanations
Karthikeyan Natesan Ramamurthy (IBM Research) · Bhanukiran Vinzamuri (IBM Research) · Yunfeng Zhang (IBM Research) · Amit Dhurandhar (IBM Research)

如何学习有用的评论家?基于模型的动作梯度估计器策略优化
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization
Pierluca D’Oro (MILA) · Wojciech Jaśkowski (NNAISENSE SA)

学习适应不断发展的领域
Learning to Adapt to Evolving Domains
Hong Liu (Tsinghua University) · Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University) · Yu Wang (Tsinghua Univ.)

图神经网络的基于路径积分的卷积和池化
Path Integral Based Convolution and Pooling for Graph Neural Networks
Zheng Ma (Princeton University) · Junyu Xuan (University of Technology Sydney) · Yu Guang Wang (University of New South Wales; MPI MiS) · Ming Li (Zhejiang Normal University) · Pietro Liò (University of Cambridge)

求解器:从微分物理学中学习与迭代PDE求解器交互
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
Kiwon Um (Telecom Paris, IP Paris) · Yun (Raymond) Fei (Columbia University) · Philipp Holl (Technical University of Munich) · Robert Brand (Technical University of Munich) · Nils Thuerey (Technical University of Munich)

通用量化神经压缩
Universally Quantized Neural Compression
Eirikur Agustsson (Google) · Lucas Theis (Twitter)

不变传播的无监督表示学习
Unsupervised Representation Learning by Invariance Propagation
Feng Wang (Tsinghua University) · Huaping Liu (Tsinghua University) · Di Guo (Tsinghua University) · Sun Fuchun (Tsinghua university)

FedSplit:用于快速联合优化的算法框架
FedSplit: an algorithmic framework for fast federated optimization
Reese Pathak (University of California, Berkeley) · Martin Wainwright (UC Berkeley)

分解式MDP中的强化学习:Oracle高效算法和非渐进设置的更严格的后悔范围
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting
Ziping Xu (University of Michigan) · Ambuj Tewari (University of Michigan)

随机平滑的高阶证明
Higher-Order Certification For Randomized Smoothing
Jeet Mohapatra (MIT) · Ching-Yun Ko (MIT) · Tsui-Wei Weng (MIT) · Pin-Yu Chen (IBM Research AI) · Sijia Liu (MIT-IBM Watson AI Lab, IBM Research) · Luca Daniel (Massachusetts Institute of Technology)

去噪扩散概率模型
Denoising Diffusion Probabilistic Models
Jonathan Ho (UC Berkeley) · Ajay Jain (UC Berkeley) · Pieter Abbeel (UC Berkeley & covariant.ai)

通过主动学习找到决策边界的同质性
Finding the Homology of Decision Boundaries with Active Learning
Weizhi Li (Arizona State University) · Gautam Dasarathy (Arizona State University) · Karthikeyan Natesan Ramamurthy (IBM Research) · Visar Berisha (Arizona State University)

超双曲线表示学习
Ultrahyperbolic Representation Learning
Law Marc (NVIDIA) · Jos Stam (NVIDIA)

现代Hopfield网络和免疫分类法的注意
Modern Hopfield Networks and Attention for Immune Repertoire Classification
Michael Widrich (LIT AI Lab / University Linz) · Bernhard Schäfl (JKU Linz) · Milena Pavlović (Department of Informatics, University of Oslo) · Hubert Ramsauer (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) · Lukas Gruber (Johannes Kepler University) · Markus Holzleitner (LIT AI Lab / University Linz) · Johannes Brandstetter (LIT AI Lab / University Linz) · Geir Kjetil Sandve (Department of Informatics, University of Oslo) · Victor Greiff (Department of Immunology, University of Oslo) · Sepp Hochreiter (LIT AI Lab / University Linz / IARAI) · Günter Klambauer (LIT AI Lab / University Linz)

HiPPO:具有最佳多项式投影的循环记忆
HiPPO: Recurrent Memory with Optimal Polynomial Projections
Albert Gu (Stanford) · Tri Dao (Stanford University) · Stefano Ermon (Stanford) · Atri Rudra (University at Buffalo, SUNY) · Christopher Ré (Stanford)

对线性上下文强盗的对抗攻击
Adversarial Attacks on Linear Contextual Bandits
Evrard Garcelon (Facebook AI Research) · Baptiste Roziere (Facebook AI Research) · Laurent Meunier (Dauphine University - FAIR Paris) · Jean Tarbouriech (Facebook AI Research Paris & Inria Lille) · Olivier Teytaud (Facebook) · Alessandro Lazaric (Facebook Artificial Intelligence Research) · Matteo Pirotta (Facebook AI Research)

为渐进式矩阵智能测试生成正确答案
Generating Correct Answers for Progressive Matrices Intelligence Tests
Niv Pekar (Tel Aviv University) · Yaniv Benny (Tel Aviv University) · Lior Wolf (Facebook AI Research)

通过软干预进行公平的多重决策
Fair Multiple Decision Making Through Soft Interventions
Yaowei Hu (University of Arkansas) · Yongkai Wu (Clemson University) · Lu Zhang (University of Arkansas) · Xintao Wu (University of Arkansas)

图像复原的神经稀疏表示
Neural Sparse Representation for Image Restoration
Yuchen Fan (University of Illinois at Urbana-Champaign) · Jiahui Yu (UIUC) · Yiqun Mei (University of Illinois) · Yulun Zhang (Northeastern University) · Yun Fu (Northeastern University) · Ding Liu (Bytedance AI Lab) · Thomas Huang (University of Illinois)

正规化线性自动编码器可恢复主要成分,最终
Regularized linear autoencoders recover the principal components, eventually
Xuchan Bao (University of Toronto) · James Lucas (University of Toronto) · Sushant Sachdeva (University of Toronto) · Roger Grosse (University of Toronto)

可变推断的鲁棒,准确的随机优化
Robust, Accurate Stochastic Optimization for Variational Inference
Akash Kumar Dhaka (Aalto University) · Alejandro Catalina (Aalto University) · Michael Andersen (Aalto University) · Måns Magnusson (Aalto University) · Jonathan Huggins (Boston University) · Aki Vehtari (Aalto University)

贝叶斯神经网络对基于梯度的对抗攻击的鲁棒性
Robustness of Bayesian Neural Networks to Gradient-Based Adversarial Attacks
Ginevra Carbone (University of Trieste) · Matthew Wicker (University of Oxford) · Luca Laurenti (University of Oxford) · Andrea Patane’ (University of Oxford) · Luca Bortolussi (University of Trieste, Department of Mathematics and Geosciences) · Guido Sanguinetti (University of Edinburgh)

超参数线性回归中的最优加权ℓ2正则化
On the Optimal Weighted ℓ2 Regularization in Overparameterized Linear Regression
Denny Wu (University of Toronto & Vector Institute) · Ji Xu (Columbia University)

策略空间中的一阶约束优化
First Order Constrained Optimization in Policy Space
Yiming Zhang (New York University) · Quan Vuong (University of California, San Diego) · Keith Ross (NYU Shanghai)

神经流形常微分方程
Neural Manifold Ordinary Differential Equations
Aaron Lou (Cornell University) · Derek Lim (Cornell University) · Isay Katsman (Cornell University) · Leo Huang (Cornell University) · Qingxuan Jiang (Cornell University) · Ser Nam Lim (Facebook AI) · Christopher De Sa (Cornell)

多类分类的理论见解:高维渐近视图
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View
Christos Thrampoulidis (UCSB) · Samet Oymak (University of California Berkeley) · Mahdi Soltanolkotabi (University of Southern california)

仔细研究精度与稳健性
A Closer Look at Accuracy vs. Robustness
Yao-Yuan Yang (UCSD) · Cyrus Rashtchian (UCSD) · Hongyang Zhang (TTIC) · Russ Salakhutdinov (Carnegie Mellon University) · Kamalika Chaudhuri (UCSD)

无监督视觉特征学习的参数实例分类
Parametric Instance Classification for Unsupervised Visual Feature learning
Yue Cao (Microsoft Research) · Zhenda Xie (Tsinghua University) · Bin Liu (Tsinghua University) · Yutong Lin (Xi’an Jiaotong University) · Zheng Zhang (MSRA) · Han Hu (Microsoft Research Asia)

社会选择的顺利可能性
The Smoothed Possibility of Social Choice
Lirong Xia (RPI)

深度传播神经网络通过反向传播的时间尖峰序列学习
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
Wenrui Zhang (University of California, Santa Barbara) · Peng Li (University of California, Santa Barbara)

了解近似Fisher信息以在宽神经网络中快速收敛自然梯度下降
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks
Ryo Karakida (National Institute of Advanced Industrial Science and Technology) · Kazuki Osawa (Tokyo Institute of Technology)

大脑可以反向传播吗?
Can the Brain Do Backpropagation?
Yuhang Song (University of Oxford) · Thomas Lukasiewicz (University of Oxford) · Zhenghua Xu (Hebei University of Technology) · Rafal Bogacz (University of Oxford)

联合学习以进行转移学习
Co-Tuning for Transfer Learning
Kaichao You (Tsinghua University) · Zhi Kou (Tsinghua University) · Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University)

神经体系结构搜索的编码研究
A Study on Encodings for Neural Architecture Search
Colin White (RealityEngines.AI) · Willie Neiswanger (Carnegie Mellon University) · Sam Nolen (RealityEngines.AI) · Yash Savani (RealityEngines.AI)

域自适应作为图形模型的推理问题
Domain Adaptation as a Problem of Inference on Graphical Models
Kun Zhang (CMU) · Mingming Gong (University of Melbourne) · Petar Stojanov (Carnegie Mellon Univerisity) · Biwei Huang (Carnegie Mellon University) · QINGSONG LIU (Unisound Intelligence Co., Ltd.) · Clark Glymour (Carnegie Mellon University)

马尔可夫变压器的级联文本生成
Cascaded Text Generation with Markov Transformers
Yuntian Deng (Harvard University) · Alexander Rush (Cornell University)

伽玛模型:无限时间预测的生成时间差异学习
Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction
Michael Janner (UC Berkeley) · Igor Mordatch (Google) · Sergey Levine (UC Berkeley)

ARMA网络:扩大致密预测的接受范围
ARMA Nets: Expanding Receptive Field for Dense Prediction
Jiahao Su (University of Maryland) · Shiqi Wang (Nanjing University ) · Furong Huang (University of Maryland)

图上的随机深高斯过程
Stochastic Deep Gaussian Processes over Graphs
Naiqi Li (Tsinghua-Berkeley Shenzhen Institute) · Wenjie Li (Tsinghua University) · Jifeng Sun (Tsinghua University) · Yinghua Gao (Tsinghua University) · Yong Jiang (Tsinghua) · Shu-Tao Xia (Tsinghua University)

Rankmax:Softmax函数的自适应投影替代方案
Rankmax: An Adaptive Projection Alternative to the Softmax Function
Weiwei Kong (Georgia Institute of Technology) · Walid Krichene (Google) · Nicolas E Mayoraz (Google, Inc.) · Steffen Rendle (Google) · Li Zhang (Google)

公平的可伸缩近似算法
Scalable Approximation Algorithm for Fair
k

中心聚类
center Clustering
Elfarouk Harb (Hong Kong University of Science and Technology) · Ho Shan Lam (The Hong Kong University of Science and Technology)

具有自适应特征库和不确定区域细化的视频对象分割
Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
Yongqing Liang (Louisiana State University) · Xin Li (Louisiana State University) · Navid Jafari (Louisiana State University) · Jim Chen (Northeastern University)

对树群的有效对抗
An Efficient Adversarial Attack for Tree Ensembles
Chong Zhang (UCLA) · Huan Zhang (UCLA) · Cho-Jui Hsieh (UCLA)

并非所有未标记的数据都相等:在半监督学习中学习加权数据
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning
Zhongzheng Ren (UIUC) · Raymond Yeh (University of Illinois at Urbana–Champaign) · Alexander Schwing (University of Illinois at Urbana-Champaign)

MPNet:用于语言理解的蒙版和置换式预训练
MPNet: Masked and Permuted Pre-training for Language Understanding
Kaitao Song (Nanjing University of Science and technology) · Xu Tan (Microsoft Research) · Tao Qin (Microsoft Research) · Jianfeng Lu (Nanjing University of Science and Technology) · Tie-Yan Liu (Microsoft Research Asia)

从多个观察结果的行为克隆中对抗模仿者
Fighting Copycat Agents in Behavioral Cloning from Multiple Observations
Chuan Wen (Tsinghua University) · Jierui Lin (University of California, Berkeley) · Trevor Darrell (UC Berkeley) · Dinesh Jayaraman (University of Pennsylvania) · Yang Gao (UC Berkeley)

自蒸馏放大希尔伯特空间中的正则化
Self-Distillation Amplifies Regularization in Hilbert Space
Hossein Mobahi (Google Research) · Mehrdad Farajtabar (DeepMind) · Peter Bartlett (UC Berkeley)

GreedyFool:失真感知的稀疏对抗攻击
GreedyFool: Distortion-Aware Sparse Adversarial Attack
Xiaoyi Dong (University of Science and Technology of China) · Dongdong Chen (Microsoft Cloud AI) · Jianmin Bao (Microsoft Research) · Chuan Qin (University of Science and Technology of China) · Lu Yuan (Microsoft) · Weiming Zhang (University of Science and Technology of China) · Nenghai Yu (University of Science and Technology of China) · Dong Chen (Microsoft Research Asia)

具有0-1损失和性能保证的Minimax分类
Minimax classification with 0-1 loss and performance guarantees
Santiago Mazuelas (Basque Center for Applied Mathematics) · Andrea Zanoni (Ecole Polytechnique Federale de Lausanne) · Aritz Pérez (Basque Center for Applied Mathematics (BCAM))

动态治疗方案的梯度正则V学习
Gradient Regularized V-Learning for Dynamic Treatment Regimes
Yao Zhang (University of Cambridge) · Mihaela van der Schaar (University of Cambridge)

黑匣子之外的学习:对可解释模型的追求
Learning outside the Black-Box: The pursuit of interpretable models
Jonathan Crabbe (University of Cambridge) · Yao Zhang (University of Cambridge) · William Zame (UCLA) · Mihaela van der Schaar (University of Cambridge)

通过渠道交换进行深度多模式融合
Deep Multimodal Fusion by Channel Exchanging
Yikai Wang (Tsinghua University) · Wenbing Huang (Tsinghua University) · Fuchun Sun (Tsinghua) · Tingyang Xu (Tencent AI Lab) · Yu Rong (Tencent AI Lab) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

FracTrain:有效地DNN培训在时间和空间上局部压缩位节省
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
Yonggan Fu (Rice University) · Haoran You (Rice University) · Yang Zhao (Rice University) · Yue Wang (Rice University) · Chaojian Li (Rice University) · Kailash Gopalakrishnan (IBM Research) · Zhangyang Wang (University of Texas at Austin) · Yingyan Lin (Rice University)

利用Sinkhorn散度进行更快的Wasserstein距离估计
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence
Lénaïc Chizat (CNRS) · Pierre Roussillon (ENS) · Flavien Léger (ENS) · François-Xavier Vialard (University Gustave Eiffel) · Gabriel Peyré (CNRS and ENS)

规范无法解释深度学习中的隐式正则化
Implicit Regularization in Deep Learning May Not Be Explainable by Norms
Noam Razin (Tel Aviv University) · Nadav Cohen (Tel Aviv University)

通过双向传播的可扩展图神经网络
Scalable Graph Neural Networks via Bidirectional Propagation
Ming Chen (Renmin University of China) · Zhewei Wei (Renmin University of China) · Bolin Ding (“Data Analytics and Intelligence Lab, Alibaba Group”) · Yaliang Li (Alibaba Group) · Ye Yuan ( Beijing Institute of Technology) · Xiaoyong Du (Renmin University of China) · Ji-Rong Wen (Renmin University of China)

具有辅助信息的在线矩阵填写
Online Matrix Completion with Side Information
Mark Herbster (University College London) · Stephen Pasteris (University College London) · Fai Yu Lisa Tse (University College London)

没有子类留下:粗粒度分类问题中的细粒度鲁棒性
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
Nimit S Sohoni (Stanford University) · Jared Dunnmon (Stanford University) · Geoffrey Angus (Stanford University) · Albert Gu (Stanford) · Christopher Ré (Stanford)

通过转移目标来提升一阶方法:最坏情况发生率更高的新方案
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst Case Rates
Kaiwen Zhou (The Chinese University of Hong Kong) · Anthony Man-Cho So (CUHK) · James Cheng (The Chinese University of Hong Kong)

基于深度能量的离散物理建模
Deep Energy-based Modeling of Discrete-Time Physics
Takashi Matsubara (Osaka University) · Ai Ishikawa (Kobe University) · Takaharu Yaguchi (Kobe University)

UnModNet:学习为高动态范围成像解开模图像
UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging
Chu Zhou (Peking University) · Hang Zhao (MIT) · Jin Han (Peking University) · Chang Xu (University of Sydney) · Chao Xu (Peking University) · Tiejun Huang (Peking University) · Boxin Shi (Peking University)

学习有效地寻找因果关系接近最佳的治疗方法
Learning to search efficiently for causally near-optimal treatments
Samuel Håkansson (Chalmers University of Technology) · Viktor Lindblom (Chalmers University of Technology) · Omer Gottesman (Harvard University) · Fredrik Johansson (Chalmers University of Technology)

监督式对比学习
Supervised Contrastive Learning
Prannay Khosla (Google LLC) · Piotr Teterwak (Google) · Chen Wang (Google) · Aaron Sarna (Google) · Yonglong Tian (MIT) · Phillip Isola (Massachusetts Institute of Technology) · Aaron Maschinot (Google Research) · Ce Liu (Google) · Dilip Krishnan (Google)

两人零和游戏的均值分析
A mean-field analysis of two-player zero-sum games
Carles Domingo-Enrich (NYU) · Samy Jelassi (Princeton University) · Arthur Mensch (ENS) · Grant Rotskoff (New York University) · Joan Bruna (NYU)

GROVER:大型分子图上的自监管消息传递变压器
GROVER: Self-Supervised Message Passing Transformer on Large-scale Molecular Graphs
Yu Rong (Tencent AI Lab) · Yatao Bian (Tencent AI Lab) · Tingyang Xu (Tencent AI Lab) · Weiyang Xie (Tencent AI Lab) · Ying WEI (Tencent AI Lab) · Wenbing Huang (Tsinghua University) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

通过形状语义GAN从大脑活动重建感知图像
Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN
Tao Fang (Zhejiang University) · Yu Qi (Zhejiang University) · Gang Pan (Zhejiang University)

基于张量分解的对偶诱导正则化器的知识图完成
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
Zhanqiu Zhang (University of Science and Technology of China) · Jianyu Cai (University of Science and Technology of China) · Jie Wang (University of Science and Technology of China)

贝叶斯伪核集
Bayesian Pseudocoresets
Dionysis Manousakas (University of Cambridge) · Zuheng Xu (University of British Columbia) · Cecilia Mascolo (University of Cambridge) · Trevor Campbell (UBC)

Set2Graph:从集合中学习图
Set2Graph: Learning Graphs From Sets
Hadar Serviansky (Weizmann Institute of Science) · Nimrod Segol (Weizmann Institute of Science) · Jonathan Shlomi (Weizmann Institute of Science) · Kyle Cranmer (New York University) · Eilam Gross (Weizmann Institute of Science) · Haggai Maron (NVIDIA Research) · Yaron Lipman (Weizmann Institute of Science)

仔细研究现代元学习的培训策略
A Closer Look at the Training Strategy for Modern Meta-Learning
JIAXIN CHEN (The Hong Kong Polytechnic University) · Xiao-Ming Wu (The Hong Kong Polytechnic University) · Yanke Li (ETH Zurich) · Qimai LI (The Hong Kong PolyU) · Li-Ming Zhan (The Hong Kong Polytechnic University) · Fu-lai Chung (The Hong Kong Polytechnic University)

硬形约束内核机
Hard Shape-Constrained Kernel Machines
Pierre-Cyril Aubin-Frankowski (MINES ParisTech) · Zoltan Szabo (Ecole Polytechnique)

通过策略规范化促进多主体深度强化学习中的协调
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning
Julien Roy (Mila) · Paul Barde (Quebec AI institute - Mila, McGill) · Félix G Harvey (Polytechnique Montréal) · Derek Nowrouzezahrai (McGill University) · Chris Pal (MILA, Polytechnique Montréal, Element AI)

强盗线性控制
Bandit Linear Control
Asaf Cassel (Tel Aviv University) · Tomer Koren (Tel Aviv University and Google)

在域之间调整神经体系结构
Adapting Neural Architectures Between Domains
Yanxi Li (University of Sydney) · zhaohui yang (peking university) · Yunhe Wang (Huawei Noah’s Ark Lab) · Chang Xu (University of Sydney)

在胶囊网络中引入路由不确定性
Introducing Routing Uncertainty in Capsule Networks
Fabio De Sousa Ribeiro (University of Lincoln) · Georgios Leontidis (University of Aberdeen) · Stefanos Kollias (University of Lincoln)

精确依赖深度的ReLU网络的清晰表示定理
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth
Guy Bresler (MIT) · Dheeraj Nagaraj (Massachusetts Institute of Technology)

拉普拉斯和神经正切核之间的相似性
On the Similarity between the Laplace and Neural Tangent Kernels
Amnon Geifman (Weizmann Institute) · Abhay Yadav (University of Maryland) · Yoni Kasten (Weizmann Institute) · Meirav Galun (Weizmann Institute of Science) · David Jacobs (University of Maryland) · Basri Ronen (Weizmann Inst.)

不再有偏差:对抗性强盗和MDP的高概率数据依赖后悔界限
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
Chung-Wei Lee (University of Southern California) · Haipeng Luo (University of Southern California) · Chen-Yu Wei (University of Southern California) · Mengxiao Zhang (University of Southern California)

广义视频帧插值
Generalized Video Frame Interpolation
Youjian Zhang (the University of Sydney) · Chaoyue Wang (University of Sydney) · Dacheng Tao (University of Sydney)

求解非凸极小极大问题的混合方差降低SGD算法
Hybrid Variance-Reduced SGD Algorithms for Nonconvex-Concave Minimax Problems
Quoc Tran Dinh (Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, North Carolina) · Deyi Liu (University of North Carolina) · Lam Nguyen (IBM Research, Thomas J. Watson Research Center)

大规模市场均衡计算的一阶方法
First-order methods for large-scale market equilibrium computation
Yuan Gao (Columbia University) · Christian Kroer (Columbia University)

黎曼流形上的马斯特高斯过程
Matern Gaussian processes on Riemannian manifolds
Viacheslav Borovitskiy (St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences (PDMI RAS)) · Alexander Terenin (Petuum, Inc. and Imperial College London) · Peter Mostowsky (St. Petersburg State University) · Marc Deisenroth (University College London)

非政策评估和政策优化的Minimax置信区间
Minimax Confidence Interval for Off-Policy Evaluation and Policy Optimization
Nan Jiang (University of Illinois at Urbana-Champaign) · Jiawei Huang (University of Illinois at Urbana-Champaign)

不规则时间序列的神经控制微分方程
Neural Controlled Differential Equations for Irregular Time Series
Patrick Kidger (University of Oxford) · James Morrill (University of Oxford) · James Foster (University of Oxford) · Terry Lyons (University of Oxford)

AutoPrivacy:用于安全神经网络推理的自动分层参数选择
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference
Qian Lou (Indiana University Bloomington) · Song Bian (Kyoto University) · Lei Jiang (Indiana University Bloomington)

无限视野强化学习中的混杂鲁棒策略评估
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning
Nathan Kallus (Cornell University) · Angela Zhou (Cornell University)

准独立性的内核测试
A kernel test for quasi-independence
Tamara Fernandez (University College London) · Wenkai Xu (Gatsby Unit, UCL) · Marc Ditzhaus (TU Dortmund University) · Arthur Gretton (Gatsby Unit, UCL)

字符串空间的贝叶斯优化
Bayesian Optimization over String Spaces
Henry Moss (Lancaster University) · David Leslie (Lancaster University and PROWLER.io) · Daniel Beck (University of Melbourne) · Javier Gonzalez (Amazon.com) · Paul Rayson (Lancaster University)

同时学习已知过渡的随机和对抗情节式MDP
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
Tiancheng Jin (University of Southern California) · Haipeng Luo (University of Southern California)

Audeo:产生静音表演视频的音频
Audeo: Audio Generation for a Silent Performance Video
Kun Su (University of Washington Seattle) · Xiulong Liu (University of Washington) · Eli Shlizerman (Departments of Applied Mathematics and Electrical & Computer Engineering, University of Washington Seattle)

BoTorch的模块化贝叶斯优化:一种有效的可微分蒙特卡罗方法
Modular Bayesian Optimization with BoTorch: An Efficient Differentiable Monte-Carlo Approach
Maximilian Balandat (Facebook) · Brian Karrer (Facebook) · Daniel Jiang (Facebook) · Samuel Daulton (Facebook) · Ben Letham (Facebook) · Andrew Gordon Wilson (New York University) · Eytan Bakshy (Facebook)

通过差分隐私的对抗鲁棒流算法
Adversarially Robust Streaming Algorithms via Differential Privacy
Avinatan Hasidim (Google) · Haim Kaplan (TAU, GOOGLE) · Yishay Mansour (Tel Aviv University / Google) · Yossi Matias (Google) · Uri Stemmer (Ben-Gurion University)

认证的单调神经网络
Certified Monotonic Neural Networks
Xingchao Liu (University of Texas at Austin) · Xing Han (The University of Texas at Austin) · Na Zhang (Tsinghua University) · Qiang Liu (UT Austin)

集成多域结果以学习最佳个性化治疗规则
Integrating Multi-domain Outcomes for Learning Optimal Individualized Treatment Rules
Yuan Chen (Columbia University) · Donglin Zeng (University of North Carolina at Chapel Hill) · Tianchen Xu (Columbia University) · Yuanjia Wang (Columbia University)

多尺度深度均衡模型
Multiscale Deep Equilibrium Models
Shaojie Bai (Carnegie Mellon University) · Vladlen Koltun (Intel Labs) · J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

ShapeFlow:3D形状之间可学习的变形流
ShapeFlow: Learnable Deformation Flows Among 3D Shapes
Chiyu Jiang (UC Berkeley) · Jingwei Huang (Stanford University) · Andrea Tagliasacchi (Google Research, Brain) · Leonidas J Guibas (stanford.edu)

BlockGAN:从未标记的图像中学习3D对象感知场景表示
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images
Thu Nguyen-Phuoc (University of Bath) · Christian Richardt (University of Bath) · Long Mai (Adobe Research) · Yongliang Yang (University of Bath) · Niloy Mitra (University College London)

通过子采样相似性查询提高DBSCAN的速度
Faster DBSCAN via subsampled similarity queries
Heinrich Jiang (Google Research) · Jennifer Jang (Uber) · Jakub Lacki (Google)

小波流:快速训练高分辨率归一化流
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
Jason Yu (York University) · Konstantinos Derpanis (Ryerson University) · Marcus Brubaker (York University)

通过逆向强化学习来学习回顾性知识
Learning Retrospective Knowledge with Reverse Reinforcement Learning
Shangtong Zhang (University of Oxford) · Vivek Veeriah (University of Michigan) · Shimon Whiteson (University of Oxford)

概率主动元学习
Probabilistic Active Meta-Learning
Jean Kaddour (Imperial College London) · Steindor Saemundsson (Imperial College London) · Marc Deisenroth (University College London)

通过协变量移位适应提高针对常见腐败的鲁棒性
Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider (University of Tübingen) · Evgenia Rusak (University of Tuebingen) · Luisa Eck (LMU Munich) · Oliver Bringmann (University of Tübingen) · Wieland Brendel (AG Bethge, University of Tübingen) · Matthias Bethge (University of Tübingen)

训练生成对抗网络的分散并行算法
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets
Mingrui Liu (Boston University) · Wei Zhang (IBM T.J.Watson Research Center) · Youssef Mroueh (IBM T.J Watson Research Center) · Xiaodong Cui (IBM T. J. Watson Research Center) · Jarret Ross (IBM) · Tianbao Yang (The University of Iowa) · Payel Das (IBM Research)

通过低阶高斯Copula量化不确定性的矩阵完成
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
Yuxuan Zhao (Cornell University) · Madeleine Udell (Cornell University)

结合深度强化学习和不完全信息游戏的搜索
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
Noam Brown (Facebook AI Research) · Anton Bakhtin (Facebook AI Research) · Adam Lerer (Facebook AI Research) · Qucheng Gong (Facebook AI Research)

最佳自适应电极选择,以最大化同时记录的神经元产量
Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield
John Choi (New York University) · Krishan Kumar (New York University) · Mohammad Khazali (New York University) · Katie Wingel (New York University) · Mahdi Choudhury (New York University) · Adam Charles (Johns Hopkins University) · Bijan Pesaran (New York University)

高维稀疏线性强盗
High-Dimensional Sparse Linear Bandits
Botao Hao (Deepmind) · Tor Lattimore (DeepMind) · Mengdi Wang (Princeton University)

稳健的最优运输及其在生成建模和领域适应中的应用
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation
Yogesh Balaji (University of Maryland) · Rama Chellappa (University of Maryland College Park) · Soheil Feizi (University of Maryland)

信息混淆中对抗表示学习的权衡与保证
Trade-offs and Guarantees on Adversarial Representation Learning for Information Obfuscation
Han Zhao (Carnegie Mellon University) · Jianfeng Chi (University of Virginia) · Yuan Tian (University of Virginia) · Geoffrey Gordon (MSR Montréal & CMU)

贝叶斯风险度量优化
Bayesian Optimization of Risk Measures
Sait Cakmak (Georgia Institute of Technology) · Raul Astudillo Marban (Cornell University) · Peter Frazier (Cornell / Uber) · Enlu Zhou (Georgia Institute of Technology)

去噪平滑:适用于预训练分类器的防御
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Hadi Salman (Microsoft Research AI) · Mingjie Sun (Carnegie Mellon University) · Greg Yang (Microsoft Research) · Ashish Kapoor (Microsoft) · J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

多武装土匪的批量粗排名
Batched Coarse Ranking in Multi-Armed Bandits
Nikolai Karpov (Indiana University Bloomington) · Qin Zhang (Indiana University Bloomington)

Bandit-PAM:通过多臂土匪进行的几乎线性时间k-类聚类
Bandit-PAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits
Mo Tiwari (Stanford University) · Martin Zhang (Harvard University) · James J Mayclin (Stanford University) · Sebastian Thrun (Stanford University) · Chris Piech (Stanford) · Ilan Shomorony (University of Illinois at Urbana Champaign)

树上的快速不平衡最优运输
Fast Unbalanced Optimal Transport on Tree
Ryoma Sato (Kyoto University) · Makoto Yamada (Kyoto University/RIKEN AIP) · Hisashi Kashima (Kyoto University/RIKEN Center for AIP)

对比学习与对抗性例子
Contrastive Learning with Adversarial Examples
Chih-Hui Ho (University of California San Diego) · Nuno Nvasconcelos (UC San Diego)

弱监督的深层功能图,用于形状匹配
Weakly Supervised Deep Functional Maps for Shape Matching
Abhishek Sharma (Ecole Polytechnique) · Maks Ovsjanikov (Ecole polytechnique)

百特置换过程
Baxter Permutation Process
Masahiro Nakano (NTT communication science laboratories) · Akisato Kimura (NTT Communication Science Laboratories) · Takeshi Yamada (NTT Communication Science Labs.) · Naonori Ueda (NTT)

运行时混淆下的反事实预测
Counterfactual Predictions under Runtime Confounding
Amanda Coston (Carnegie Mellon University) · Edward Kennedy (Carnegie Mellon University) · Alexandra Chouldechova (CMU)

对抗式学习的低成本成本显着目标检测
Few-Cost Salient Object Detection with Adversarial-Paced Learning
Dingwen Zhang (Xidian University) · HaiBin Tian (Xidian University) · Jungong Han (University of Warwick)

参数偏微分方程的多极图神经算子
Multipole Graph Neural Operator for Parametric Partial Differential Equations
Zongyi Li (Caltech) · Nikola Kovachki (California Institute of Technology) · Kamyar Azizzadenesheli (Caltech) · Burigede Liu (caltech) · Andrew Stuart (California Institute of Technology) · Kaushik Bhattacharya (Caltech) · Anima Anandkumar (NVIDIA / Caltech)

广义提升
Generalized Boosting
Arun Suggala (Carnegie Mellon University) · Bingbin Liu () · Pradeep Ravikumar (Carnegie Mellon University)

图神经网络的参数化解释器
Parameterized Explainer for Graph Neural Network
Dongsheng Luo (The Pennsylvania State University) · Wei Cheng (NEC Labs America) · Dongkuan Xu (The Pennsylvania State University) · Wenchao Yu (UCLA) · Bo Zong (NEC Labs) · Haifeng Chen (NEC Labs America) · Xiang Zhang (The Pennsylvania State University)

一次嵌入的所有单词嵌入
All Word Embeddings from One Embedding
Sho Takase (Tokyo Institute of Technology) · Sosuke Kobayashi (Preferred Networks)

本地差异私密(上下文)土匪学习
Locally Differentially Private (Contextual) Bandits Learning
Kai Zheng (Kuaishou) · Tianle Cai (Peking University) · Weiran Huang (Noah’s Ark Lab) · Zhenguo Li (Noah’s Ark Lab, Huawei Tech Investment Co Ltd) · Liwei Wang (Peking University)

多标签分类:Hamming损失和子集准确性是否真的相互冲突?
Multi-Label Classification: Does Hamming Loss and Subset Accuracy really conflict with each other?
Guoqiang Wu (Tsinghua University) · Jun Zhu (Tsinghua University)

浅层ReLU模型中粗麻布的解析特征:一个对称的故事
Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry
Yossi Arjevani (NYU) · Michael Field (UC Santa Barbara)

邻域控制文法增强分子优化
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars
Chencheng Xu (Tsinghua University) · Qiao Liu (Tsinghua University) · Minlie Huang (Tsinghua University) · Tao Jiang (University of California - Riverside)

具有部分可观察的连续非线性动力学的逆理性控制
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
Minhae Kwon (Rice University, Baylor College of Medicine) · Saurabh Daptardar (Google) · Paul R Schrater (University of Minnesota) · Zachary Pitkow (BCM/Rice)

具有探索增强功能的等效空间中的可微神经架构搜索
Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement
Miao Zhang (UTS&BIT) · Huiqi Li (Beijing Institute of Technology) · Shirui Pan (Monash University) · Xiaojun Chang (Monash University) · Zongyuan Ge (Monash University) · Steven Su (University of Technology Sydney)

高维采样问题中Langevin Monte Carlo的方差减少
Variance reduction for Langevin Monte Carlo in high dimensional sampling problems
ZHIYAN DING (University of Wisconsin-Madison) · Qin Li (University of Wisconsin-Madison)

在线结构化元学习
Online Structured Meta-learning
Huaxiu Yao (Pennsylvania State University) · Yingbo Zhou (Salesforce Research) · Mehrdad Mahdavi (Pennsylvania State University) · Zhenhui (Jessie) Li (Penn State University) · Richard Socher (Salesforce) · Caiming Xiong (Salesforce)

从分层分组中学习自我监督的视觉表示
Self-Supervised Visual Representation Learning from Hierarchical Grouping
Xiao Zhang (University of Chicago) · Michael Maire (University of Chicago)

MiniLM:用于预训练变压器的与任务无关的压缩的深度自注意蒸馏
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Wenhui Wang (MSRA) · Furu Wei (Microsoft Research Asia) · Li Dong (Microsoft Research) · Hangbo Bao (Harbin Institute of Technology) · Nan Yang (Microsoft Research Asia) · Ming Zhou (Microsoft Research)

并行多目标贝叶斯优化的可预期的超量改进
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
Samuel Daulton (Facebook) · Maximilian Balandat (Facebook) · Eytan Bakshy (Facebook)

基于能量的失配检测
Energy-based Out-of-distribution Detection
Weitang Liu (University of California, Davis) · Xiaoyun Wang (University of California, Davis) · John Owens (University of California, Davis) · Sharon Yixuan Li (Stanford University)

用于未知搜索空间中贝叶斯优化的次线性后悔界
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
Hung Tran-The (Deakin University) · Sunil Gupta (Deakin University) · Santu Rana (Deakin University) · Huong Ha (Deakin University) · Svetha Venkatesh (Deakin University)

动量中的魔鬼:通过消除动量因果效应进行长尾分类
Devil in the Momentum: Long-Tailed Classification by Removing Momentum Causal Effect
Kaihua Tang (Nanyang Technological University) · Jianqiang Huang (Damo Academy, Alibaba Group) · Hanwang Zhang (NTU)

CircleGAN:跨球形圈的生成对抗性学习
CircleGAN: Generative Adversarial Learning across Spherical Circles
Woohyeon Shim (Postech) · Minsu Cho (POSTECH)

通过最大编码率降低原理学习多样化和区分性表示
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
Yaodong Yu (University of California, Berkeley) · Kwan Ho Ryan Chan (University of California, Berkeley) · Chong You (University of California, Berkeley) · Chaobing Song (Tsinghua University) · Yi Ma (UC Berkeley)

多元点过程的噪声对比估计
Noise-Contrastive Estimation for Multivariate Point Processes
Hongyuan Mei (JOHNS HOPKINS UNIVERSITY) · Tom Wan (JOHNS HOPKINS UNIVERSITY) · Jason Eisner (Johns Hopkins + Microsoft)

POMO:针对强化学习的具有多个最优策略的策略优化
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
Yeong-Dae Kwon (Samsung SDS) · Jinho Choo (Samsung SDS) · Byoungjip Kim (Samsung SDS) · Iljoo Yoon (Samsung SDS) · Youngjune Gwon (Samsung SDS) · Seungjai Min (Samsung SDS)

混合离散和连续变量的混合哈密顿量蒙特卡洛
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables
Guangyao Zhou (Vicarious AI)

数据多样化:神经机器翻译的绝佳策略
Data Diversification: An Elegant Strategy For Neural Machine Translation
Xuan-Phi Nguyen (Nanyang Technological University) · Shafiq Joty (Nanyang Technological University) · Kui Wu (Institute for Infocomm Research, Singapore) · Ai Ti Aw (Institute for Infocomm Research)

AutoBSS:用于块堆叠样式搜索的高效算法
AutoBSS: An Efficient Algorithm for Block Stacking Style Search
yikang zhang (Huawei Digital Technologies Co., Ltd.) · Jian Zhang (Huawei Technologies Co., Ltd.) · Zhao Zhong (HUAWEI)

在类似插值的条件下更快地转出鞍点
Escaping Saddle-Point Faster under Interpolation-like Conditions
Abhishek Roy (University of California, Davis) · Krishnakumar Balasubramanian (University of California, Davis) · Saeed Ghadimi (Princeton University) · Prasant Mohapatra (University of California, Davis)

神经网络无法学习周期函数及其修复方法
Neural Networks Fail to Learn Periodic Functions and How to Fix It
Ziyin Liu (University of Tokyo) · Tilman Hartwig (University of Tokyo) · Masahito Ueda (University of Tokyo)

表示学习的自我监督关系推理
Self-Supervised Relational Reasoning for Representation Learning
Massimiliano Patacchiola (University of Edinburgh) · Amos Storkey (University of Edinburgh)

模拟CNN前端的主要视觉皮层可提高图像摄动的鲁棒性
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
Joel Dapello (Harvard University) · Tiago Marques (MIT) · Martin Schrimpf (MIT) · Franziska Geiger (MIT) · David Cox (MIT-IBM Watson AI Lab) · James J DiCarlo (Massachusetts Institute of Technology)

有限孔蒂武装土匪
Finite Contiuum-Armed Bandits
Solenne Gaucher (Université Paris-Saclay)

高斯过程的多任务因果学习
Multi-task Causal Learning with Gaussian Processes
Virginia Aglietti (University of Warwick) · Theodoros Damoulas (University of Warwick & The Alan Turing Institute) · Mauricio Álvarez (University of Sheffield) · Javier Gonzalez (Amazon.com)

随机游动图神经网络
Random Walk Graph Neural Networks
Giannis Nikolentzos (Athens University of Economics and Business) · Michalis Vazirgiannis (École Polytechnique)

用有限的数据训练生成对抗网络
Training Generative Adversarial Networks with Limited Data
Tero Karras (NVIDIA) · Miika Aittala (MIT CSAIL / NVIDIA) · Janne Hellsten (NVIDIA) · Samuli Laine (NVIDIA) · Jaakko Lehtinen (Aalto University & NVIDIA) · Timo Aila (NVIDIA)

(非平衡)高斯测度之间的熵最优输运具有封闭形式
Entropic Optimal Transport between (Unbalanced) Gaussian Measures has a Closed Form
Hicham Janati (Inria / ENSAE) · Boris Muzellec (ENSAE, Institut Polytechnique de Paris) · Gabriel Peyré (CNRS and ENS) · Marco Cuturi (Google Brain & CREST - ENSAE)

自定进度的深度强化学习
Self-Paced Deep Reinforcement Learning
Pascal Klink (TU Darmstadt) · Carlo D’Eramo (TU Darmstadt) · Jan Peters (TU Darmstadt & MPI Intelligent Systems) · Joni Pajarinen (TU Darmstadt)

集成基于进化和基于梯度的策略搜索的高效异步方法
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search
Kyunghyun Lee (KAIST) · Byeong-Uk Lee (KAIST) · Ukcheol Shin (KAIST) · In So Kweon (KAIST)

线性阈值模型下的在线影响最大化
Online Influence Maximization under Linear Threshold Model
Shuai Li (Shanghai Jiao Tong University) · Fang Kong (Shanghai Jiao Tong University) · Kejie Tang (Shanghai Jiao Tong University) · Qizhi Li (Xidian University) · Wei Chen (Microsoft Research)

通过汤普森采样进行的联合贝叶斯优化
Federated Bayesian Optimization via Thompson Sampling
Zhongxiang Dai (National University of Singapore) · Bryan Kian Hsiang Low (National University of Singapore) · Patrick Jaillet (MIT)

对称逆学习
Inverse Learning of Symmetries
Mario Wieser (University of Basel) · Sonali Parbhoo (Harvard University) · Aleksander Wieczorek (University of Basel) · Volker Roth (University of Basel)

MESA:使用MEta-SAmpler进行有效的集成式失衡学习
MESA: Effective Ensemble Imbalanced Learning with MEta-SAmpler
Zhining Liu (Jilin University) · Pengfei Wei (National University of Singapore) · Jing Jiang (University of Technology Sydney) · Wei Cao (MSRA) · Jiang Bian (Microsoft) · Yi Chang (Jilin University)

ExpandNets:线性超参数化可训练紧凑型卷积网络
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks
Shuxuan Guo (EPFL) · Jose M. Alvarez (NVIDIA) · Mathieu Salzmann (EPFL)

通过纹理合成生成硬示例,用于跨域形状相似性学习
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
Shunming Li (Alibaba Group) · Huan Fu (Alibaba Group) · Rongfei Jia (Alibaba Group) · Mingming Gong (University of Melbourne) · Binqiang Zhao (Alibaba Corp) · Dacheng Tao (University of Sydney)

高保真生成图像压缩
High-Fidelity Generative Image Compression
Fabian Mentzer (ETH Zurich) · George D Toderici (Google) · Michael Tschannen (Google Brain) · Eirikur Agustsson (Google)

黑匣子VI的进步:标准化流程,重要性加权和优化
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
Abhinav Agrawal (UMass Amherst) · Daniel Sheldon (University of Massachusetts Amherst) · Justin Domke (University of Massachusetts, Amherst)

超越均值场:结构化的深高斯过程改善了预测的不确定性
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
Jakob Lindinger (Bosch Center for Artificial Intelligence) · David Reeb (Bosch Center for Artificial Intelligence (BCAI)) · Christoph Lippert (Hasso Plattner Institute for Digital Engineering, Universität Potsdam) · Barbara Rakitsch (Bosch Center for Artificial Intelligence)

具有优化随机特征的学习:无稀疏性和低等级假设的量子机器学习的指数加速
Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions
Hayata Yamasaki (The University of Tokyo) · Sathyawageeswar Subramanian (University of Cambridge) · Sho Sonoda (RIKEN AIP) · Masato Koashi (The University of Tokyo)

通过熵正则化进行域泛化
Domain Generalization via Entropy Regularization
Shanshan Zhao (The University of Sydney) · Mingming Gong (University of Melbourne) · Tongliang Liu (The University of Sydney) · Huan Fu (Alibaba Group) · Dacheng Tao (University of Sydney)

语法压缩线性代数的不可能结果
Impossibility Results for Grammar-Compressed Linear Algebra
Amir Abboud (IBM research) · Arturs Backurs (TTIC) · Karl Bringmann (Saarland University) · Marvin Künnemann (Max-Planck-Institut für Informatik)

DNN的抛物线近似线搜索
Parabolic Approximation Line Search for DNNs
Maximus Mutschler (University of Tübingen) · Andreas Zell (University of Tuebingen)

重新访问参数共享以进行自动神经通道号搜索
Revisiting Parameter Sharing for Automatic Neural Channel Number Search
Jiaxing Wang (Institute of Automation, Chinese Academy of Sciences) · Haoli Bai (The Chinese University of Hong Kong) · Jiaxiang Wu (Tencent AI Lab) · Xupeng Shi (Northeastern University) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab) · Irwin King (Chinese University of Hong Kong) · Michael Lyu (CUHK) · Jian Cheng (Institute of Automation, Chinese Academy of Sciences)

紧实的外部约束可进行Lipschitz认证的培训
Lipschitz-Certifiable Training with a Tight Outer Bound
Sungyoon Lee (Seoul National University) · Jaewook Lee (Seoul National University) · Saerom Park (Sungshin Women’s University)

使用交互机制,离散分布的局部私有非渐近测试更快
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms
Thomas Berrett (CREST, ENSAE, Institut Polytechnique de Paris) · Cristina Butucea (CREST, ENSAE, Institut Polytechnique de Paris)

凸凹极小优化的改进算法
Improved Algorithms for Convex-Concave Minimax Optimization
Yuanhao Wang (Tsinghua University) · Jian Li (Tsinghua University)

神经星域作为原始表示
Neural Star Domain as Primitive Representation
Yuki Kawana (The University of Tokyo) · Yusuke Mukuta (The University of Tokyo) · Tatsuya Harada (The University of Tokyo / RIKEN)

GAIT-prop:源自错误的反向传播的生物学上合理的学习规则
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
Nasir Ahmad (Donders Institute for Brain, Cognition and Behaviour, Radboud University) · Marcel A. J. van Gerven (Radboud Universiteit) · Luca Ambrogioni (Radboud University)

Dirichlet图变分自动编码器
Dirichlet Graph Variational Autoencoder
Jia Li (The Chinese University of Hong Kong) · Jianwei Yu (CUHK) · Jiajin Li (The Chinese University of Hong Kong) · Honglei Zhang (Georgia Institute of Technology) · Kangfei Zhao (The Chinese University of Hong Kong) · Yu Rong (Tencent AI Lab) · Hong Cheng (The Chinese University of Hong Kong) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

随机风险规避学习的自适应采样
Adaptive Sampling for Stochastic Risk-Averse Learning
Sebastian Curi (ETH Zürich) · Kfir Y. Levy (Technion) · Stefanie Jegelka (MIT) · Andreas Krause (ETH Zurich)

可动反事实推理的深层结构因果模型
Deep Structural Causal Models for Tractable Counterfactual Inference
Nick Pawlowski (Imperial College London) · Daniel Coelho de Castro (Imperial College London) · Ben Glocker (Imperial College London)

重新思考分布转移下的深度学习重要性加权
Rethinking Importance Weighting for Deep Learning under Distribution Shift
Tongtong Fang (KTH Royal Institute of Technology) · Nan Lu (University of Tokyo/ RIKEN-AIP) · Gang Niu (RIKEN) · Masashi Sugiyama (RIKEN / University of Tokyo)

通过乐观的策略搜索和计划进行基于模型的有效强化学习
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
Sebastian Curi (ETH Zürich) · Felix Berkenkamp (Bosch Center for Artificial Intelligence) · Andreas Krause (ETH Zurich)

确定性政策的双重鲁棒非政策价值和梯度估计
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies
Nathan Kallus (Cornell University) · Masatoshi Uehara (Cornell University)

基于沃森感知模型的生成神经网络的损失函数
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model
Steffen Czolbe (University of Copenhagen) · Oswin Krause (University of Copenhagen) · Ingemar Cox (University College London) · Christian Igel (University of Copenhagen)

协变量转换下的非政策评估和外部有效性学习
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift
Masatoshi Uehara (Cornell University) · Masahiro Kato (The University of Tokyo) · Shota Yasui (Cyberagent)

DeepI2I:通过从GAN传输来实现深度分层的图像到图像转换
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs
yaxing wang (Centre de Visió per Computador (CVC)) · Lu Yu (computer vision center, UAB) · Joost van de Weijer (Computer Vision Center Barcelona)

自然图网络
Natural Graph Networks
Pim de Haan (Qualcomm AI Research, University of Amsterdam) · Taco Cohen (Qualcomm AI Research) · Max Welling (University of Amsterdam / Qualcomm AI Research)

用于时间序列预测的对抗式稀疏变压器
Adversarial Sparse Transformer for Time Series Forecasting
Sifan Wu (Tsinghua University) · Xi Xiao (Tsinghua University) · Qianggang Ding (Tsinghua University) · Peilin Zhao (Tencent AI Lab) · Ying Wei (Tencent AI Lab) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

梯度提升的传递优化及泛化分析及其在多尺度图神经网络中的应用
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
Kenta Oono (The University of Tokyo, Preferred Networks Inc.) · Taiji Suzuki (The University of Tokyo/RIKEN-AIP)

3D下无监督的以对象为中心的视频生成和分解
Unsupervised object-centric video generation and decomposition in 3D
Paul Henderson (IST Austria) · Christoph Lampert (IST Austria)

弱监督语义分割的因果干预
Causal Intervention for Weakly-Supervised Semantic Segmentation
Dong Zhang (Nanjing University of Science and Technology) · Hanwang Zhang (NTU) · Jinhui Tang (Nanjing University of Science and Technology) · Xian-Sheng Hua (Damo Academy, Alibaba Group) · Qianru Sun (Singapore Management University)

分层补丁VAE-GAN:从单个样本生成多样化的视频
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
Shir Gur (Tel Aviv University) · Sagie Benaim (Tel Aviv University) · Lior Wolf (Facebook AI Research)

改进(自然)Actor-Critic算法的样本复杂度界限
Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms
Tengyu Xu (The Ohio State University) · Zhe Wang (Ohio State University) · Yingbin Liang (The Ohio State University)

使用极化正则化器的神经元级结构化修剪
Neuron-level Structured Pruning using Polarization Regularizer
Tao Zhuang (Alibaba Group) · Zhixuan Zhang (Beijing University of Posts and Telecommunications) · Yuheng Huang (Beijing Univ. of Posts and Telecommunications) · Xiaoyi Zeng (Alibaba Group) · Kai Shuang (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.) · Xiang Li (Alibaba Group)

深度生成模型对离群值检测的进一步分析
Further Analysis of Outlier Detection with Deep Generative Models
Ziyu Wang (Tsinghua University) · Bin Dai (Samsung Research China - Beijing) · David P Wipf (Microsoft Research Asia) · Jun Zhu (Tsinghua University)

TaylorGAN:邻居增强型策略更新,以实现示例高效的自然语言生成
TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation
Chun-Hsing Lin (National Taiwan University) · Siang-Ruei Wu (National Taiwan University) · Hung-yi Lee (National Taiwan University) · Yun-Nung Chen (National Taiwan University)

贝叶斯深度学习和泛化的概率视角
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
Andrew Gordon Wilson (New York University) · Pavel Izmailov (New York University)

Wasserstein分布鲁棒支持向量机的基于快速基于射影投影的增量算法
Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine
Jiajin Li (The Chinese University of Hong Kong) · Caihua Chen (Nanjing University) · Anthony Man-Cho So (CUHK)

具有相同集群查询的缠结集群的精确恢复
Exact Recovery of Mangled Clusters with Same-Cluster Queries
Marco Bressan (Sapienza University of Rome) · Nicolò Cesa-Bianchi (Università degli Studi di Milano) · Silvio Lattanzi (Google Research) · Andrea Paudice (University of Milan)

用于深层生成流的伍德伯里变换
Woodbury Transformations for Deep Generative Flows
You Lu (Virginia Tech) · Bert Huang (Virginia Tech)

自我监督的多模态多功能网络
Self-Supervised MultiModal Versatile Networks
Jean-Baptiste Alayrac (Deepmind) · Adria Recasens (DeepMind) · Rosalia Schneider (DeepMind) · Relja Arandjelović (DeepMind) · Jason Ramapuram (University of Geneva) · Jeffrey De Fauw (DeepMind) · Lucas Smaira (DeepMind) · Sander Dieleman (DeepMind) · Andrew Zisserman (DeepMind & University of Oxford)

骨架桥接点完成:从全局推断到局部调整
Skeleton-bridged Point Completion: From Global Inference to Local Adjustment
Yinyu Nie (Bournemouth University) · Xiaoguang Han (Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong (Shenzhen)) · Yiqun Lin (The Chinese University of Hong Kong, Shenzhen) · Shihui Guo (Xiamen University) · Jian Chang (Bournemouth University) · Shuguang Cui (The Chinese University of Hong Kong, Shenzhen) · Jian.J Zhang (Bournemouth University)

人工神经网络中复发与自我注意之间的权衡取舍
Untangling tradeoffs between recurrence and self-attention in artificial neural networks
Giancarlo Kerg (MILA) · Bhargav Kanuparthi (Montreal Institute for Learning Algorithms) · Anirudh Goyal ALIAS PARTH GOYAL (Université de Montréal) · Kyle Goyette (University of Montreal) · Yoshua Bengio (Mila / U. Montreal) · Guillaume Lajoie (Mila, Université de Montréal)

深入表示的贝叶斯非参数视图
A Bayesian Nonparametrics View into Deep Representations
Michał Jamroż (AGH University of Science and Technology) · Marcin Kurdziel (AGH University of Science and Technology, Krakow, Poland) · Mateusz Opala (AGH University of Science and Technology)

在鸡尾酒会方案中学习以区别方式定位探空对象
Learning to Discriminatively Localize Sounding Objects in a Cocktail-party Scenario
Di Hu (Renmin University of China) · Rui Qian (Shanghai Jiao Tong University) · Minyue Jiang (Baidu Inc.) · Xiao Tan (Baidu Inc.) · Shilei Wen (BAIDU) · Errui Ding (Baidu Inc.) · Weiyao Lin (Shanghai Jiao Tong university) · Dejing Dou (Baidu)

凸和平滑函数的动态后悔
Dynamic Regret of Convex and Smooth Functions
Peng Zhao (Nanjing University) · Yu-Jie Zhang (Nanjing University) · Lijun Zhang (Nanjing University (NJU)) · Zhi-Hua Zhou (Nanjing University)

重温弗兰克-沃尔夫的多面体:严格的互补性和稀疏性
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity
Dan Garber (Technion - Israel Institute of Technology)

Sobolev不平衡下降
Unbalanced Sobolev Descent
Youssef Mroueh (IBM T.J Watson Research Center) · Mattia Rigotti (IBM Research AI)

少量学习的信息最大化
Information Maximization for Few-Shot Learning
Malik Boudiaf (Ecole de Technologie Superieure) · Imtiaz Ziko (Ecole de technologie superieure (ETS)) · Jérôme Rony (ÉTS Montréal) · Jose Dolz (ETS Montreal) · Pablo Piantanida (CentraleSupélec - Mila) · Ismail Ben Ayed (ETS Montreal)

稀疏性和DAG约束在学习线性DAG中的作用
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
Ignavier Ng (University of Toronto) · AmirEmad Ghassami (Johns Hopkins University) · Kun Zhang (CMU)

迈向神经程序接口
Towards Neural Program Interfaces
Zachary Brown (Duke University) · Nathaniel Robinson (Brigham Young University) · David Wingate (Brigham Young University) · Nancy Fulda (Brigham Young University)

桥接视觉表示以进行对象检测
Bridging Visual Representations for Object Detection
Cheng Chi (University of Chinese Academy of Sciences) · Fangyun Wei (Microsoft Research Asia) · Han Hu (Microsoft Research Asia)

贝叶斯迭代学习优化
Bayesian Optimization for Iterative Learning
Vu Nguyen (University of Oxford) · Sebastian Schulze (University of Oxford) · Michael A Osborne (U Oxford)

RATT:经常注意用于连续图像字幕的临时任务
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning
Riccardo Del Chiaro (University of Florence) · Bartłomiej Twardowski (Computer Vision Center, UAB) · Andrew D Bagdanov (University of Florence) · Joost van de Weijer (Computer Vision Center Barcelona)

一环统治一切:具有异常值的可靠的几何感知
One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers
Heng Yang (MIT) · Luca Carlone (Massachusetts Institute of Technology)

f-GAIL:针对生成的对抗模仿学习而学习f散度
f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning
Xin Zhang (Worcester Polytechnic Institute) · Yanhua Li (“Worcester Polytechnic Institute, USA”) · Ziming Zhang (Worcester Polytechnic Institute) · Zhi-Li Zhang (University of Minnesota)

随机特征模型中2层神经网络导数的渐近正态性和置信区间
Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model
Yiwei Shen (Rutgers University) · Pierre Bellec (Rutgers)

改进的k-means ++和k-means ++并行保证
Improved Guarantees for k-means++ and k-means++ Parallel
Konstantin Makarychev (Northwestern University) · Aravind Reddy (Northwestern University) · Liren Shan (Northwestern University)

神经各向异性方向
Neural Anisotropy Directions
Guillermo Ortiz-Jimenez (EPFL) · Apostolos Modas (EPFL) · Seyed-Mohsen Moosavi-Dezfooli (ETHZ) · Pascal Frossard (EPFL)

神经网络的有效低秩高斯变分推断
Efficient Low Rank Gaussian Variational Inference for Neural Networks
Marcin Tomczak (University of Cambridge) · Siddharth Swaroop (University of Cambridge) · Richard E Turner (University of Cambridge)

折刀+-后引导程序是免费的预测性推论
Predictive inference is free with the jackknife+-after-bootstrap
Byol Kim (University of Chicago) · Chen Xu (University of Chicago) · Rina Foygel Barber (University of Chicago)

没有Lipschitz连续性的后悔范围:在线学习与相对Lipschitz损失
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses
Yihan Zhou (University of British Columbia) · Victor Sanches Portella (University of British Columbia) · Mark Schmidt (University of British Columbia) · Nicholas Harvey (University of British Columbia)

抱紧我!区分特征对深层网络边界的影响
Hold me tight! Influence of discriminative features on deep network boundaries
Guillermo Ortiz-Jimenez (EPFL) · Apostolos Modas (EPFL) · Seyed-Mohsen Moosavi-Dezfooli (ETHZ) · Pascal Frossard (EPFL)

用于设备上非接触式生命测量的多任务时移注意力网络
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
Xin Liu (University of Washington ) · Josh Fromm (OctoML) · Shwetak Patel (University of Washington) · Daniel McDuff (Microsoft Research)

学习变异语义学
Learning Mutational Semantics
Brian Hie (Massachusetts Institute of Technology) · Ellen Zhong (Massachusetts Institute of Technology) · Bryan Bryson (Massachusetts Institute of Technology) · Bonnie Berger (MIT)

再次使一键式视频对象分割有效
Make One-Shot Video Object Segmentation Efficient Again
Tim Meinhardt (TUM) · Laura Leal-Taixé (TUM)

具有金字塔形拓扑的一层广层深度网络的全球融合
Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology
Quynh N Nguyen (Saarland University) · Marco Mondelli (IST Austria)

具有少量标签的文本分类的不确定性自训练
Uncertainty-aware Self-training for Text Classification with Few Labels
Subhabrata Mukherjee (Microsoft Research) · Ahmed Awadallah (Microsoft)

在平均奖励MDP中采用最大熵方法进行非政策评估
A maximum-entropy approach to off-policy evaluation in average-reward MDPs
Nevena Lazic (DeepMind) · Dong Yin (DeepMind) · Mehrdad Farajtabar (DeepMind) · Nir Levine (DeepMind) · Dilan Gorur () · Chris Harris (Google) · Dale Schuurmans (Google Brain & University of Alberta)

重尾表示,文本极性分类和数据增强
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation
Hamid JALALZAI (Télécom ParisTech) · Pierre Colombo (Telecom ParisTech) · Chloé Clavel (Telecom-ParisTech, Paris, France) · Eric Gaussier (Université Joseph Fourier, Grenoble) · Giovanna Varni (Telecom ParisTec) · Emmanuel Vignon (IBM) · Anne Sabourin (LTCI, Telecom ParisTech, Université Paris-Saclay)

随机网络中通过Hebbian可塑性进行元学习
Meta-Learning through Hebbian Plasticity in Random Networks
Elias Najarro (IT University of Copenhagen) · Sebastian Risi (IT University of Copenhagen)

流亚模最大化中的公平性:算法和硬度
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Marwa El Halabi (MIT) · Slobodan Mitrović (MIT) · Ashkan Norouzi-Fard (Google Research) · Jakab Tardos (EPFL) · Jakub Tarnawski (Microsoft Research)

字形:快速准确地训练加密数据的深度神经网络
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
Qian Lou (Indiana University Bloomington) · Bo Feng (Indiana university) · Geoffrey Charles Fox (Indiana University) · Lei Jiang (Indiana University Bloomington)

自动重写快速梯度,而不是重写用于机器学习的外码
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients
William Moses (MIT) · Valentin Churavy (Massachussets Institute of Technology)

COT-GAN:通过因果最佳运输生成顺序数据
COT-GAN: Generating Sequential Data via Causal Optimal Transport
Tianlin Xu (London School of Economics and Political Science) · Wenliang Le (Gatsby Unit, UCL) · Michael Munn (Google) · Beatrice Acciaio (London School of Economics)

用于目标检测的细颗粒动态头
Fine-Grained Dynamic Head for Object Detection
Lin Song (Xi’an Jiaotong University) · Yanwei Li (The Chinese University of Hong Kong) · Zhengkai Jiang (Institute of Automation,Chinese Academy of Sciences) · Zeming Li (Megvii(Face++) Inc) · Hongbin Sun (Xi’an Jiaotong University) · Jian Sun (Megvii, Face++) · Nanning Zheng (Xi’an Jiaotong University)

黎曼连续归一化流
Riemannian Continuous Normalizing Flows
Emile Mathieu (University of Oxford) · Maximilian Nickel (Facebook AI Research)

用于一致性培训的无监督数据增强
Unsupervised Data Augmentation for Consistency Training
Qizhe Xie (CMU, Google Brain) · Zihang Dai (Carnegie Mellon University) · Eduard Hovy (CMU) · Thang Luong (Google Brain) · Quoc V Le (Google)

随机数据丢失的概率主成分分析的估计和归因
Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data
Aude Sportisse (Sorbonne University, Ecole Polytechnique) · Claire Boyer (LPSM, Sorbonne Université) · Julie Josses (CMAP / CNRS)

神经元Shapley:发现负责任的神经元
Neuron Shapley: Discovering the Responsible Neurons
Amirata Ghorbani (Stanford University) · James Zou (Stanford University)

论Huber污染模型下的学习伊辛模型
On Learning Ising Models under Huber’s Contamination Model
Adarsh Prasad (Carnegie Mellon University) · Vishwak Srinivasan (Carnegie Mellon University) · Sivaraman Balakrishnan (Carnegie Mellon University) · Pradeep Ravikumar (Carnegie Mellon University)

关于二元分类以外的在线学习与私人学习的对等
On the Equivalence between Online and Private Learnability beyond Binary Classification
Young H Jung (Microsoft) · Baekjin Kim (University of Michigan) · Ambuj Tewari (University of Michigan)

用于多模式分布模拟的等高线随机梯度Langevin动力学算法
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
Wei Deng (Purdue University) · Guang Lin (Purdue University) · Faming Liang (Purdue University)

从标签比例中学习:相互污染框架
Learning from Label Proportions: A Mutual Contamination Framework
Clayton Scott (University of Michigan) · Jianxin Zhang (University of Michigan)

像素级循环关联:域自适应语义分割的新视角
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
Guoliang Kang (Carnegie Mellon University) · Yunchao Wei (UTS) · Yi Yang (UTS) · Yueting Zhuang (Zhejiang University) · Alexander Hauptmann (Carnegie Mellon University)

LoCo:本地对比表示学习
LoCo: Local Contrastive Representation Learning
Yuwen Xiong (Uber ATG / University of Toronto) · Mengye Ren (University of Toronto / Uber ATG) · Raquel Urtasun (Uber ATG)

是什么为对比表示学习提供了良好的见解?
What Makes for Good Views for Contrastive Representation Learning?
Yonglong Tian (MIT) · Chen Sun (Google Research) · Ben Poole (Google Brain) · Dilip Krishnan (Google) · Cordelia Schmid (Google) · Phillip Isola (Massachusetts Institute of Technology)

深度转换不变聚类
Deep Transformation-Invariant Clustering
Tom Monnier (École des ponts Paristech) · Thibault Groueix (École des ponts ParisTech) · Mathieu Aubry (École des ponts ParisTech)

从分组观察中可识别的非参数混合模型的一致估计
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
Alexander Ritchie (University of Michigan) · Robert Vandermeulen (Technische Universität Berlin) · Clayton Scott (University of Michigan)

神经网络何时优于内核方法?
When Do Neural Networks Outperform Kernel Methods?
Behrooz Ghorbani (Stanford University) · Song Mei (Stanford University) · Theodor Misiakiewicz (Stanford University) · Andrea Montanari (Stanford)

通过对等预测进行真实数据采集
Truthful Data Acquisition via Peer Prediction
Yiling Chen (Harvard University) · Yiheng Shen (Tsinghua University) · Shuran Zheng (Harvard University)

游戏中的套期保值:外部和互换后悔的融合更快
Hedging in games: Faster convergence of external and swap regrets
Xi Chen (Columbia University) · Binghui Peng (Columbia University)

合作交流的数学理论
A mathematical theory of cooperative communication
Pei Wang (Rutgers University-Newark) · Junqi Wang (Rutgers University-Newark) · Pushpi Paranamana (Rutgers University-Newark) · Patrick Shafto (Rutgers University - Newark)

检测为回归:具有中值平滑的认证对象检测
Detection as Regression: Certified Object Detection with Median Smoothing
Ping-yeh Chiang (University of Maryland, College Park) · Michael Curry (University of Maryland) · Ahmed Abdelkader (University of Maryland, College Park) · Aounon Kumar (University of Maryland, College Park) · John Dickerson (University of Maryland) · Tom Goldstein (University of Maryland)

改进低维随机基数中的神经网络训练
Improving Neural Network Training in Low Dimensional Random Bases
Frithjof Gressmann (Graphcore) · Zach Eaton-Rosen (Graphcore) · Carlo Luschi (Graphcore)

黑匣子开膛手:使用生成进化算法复制黑匣子模型
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
Antonio Barbalau (University of Bucharest) · Adrian Cosma (Politehnica University of Bucharest) · Radu Tudor Ionescu (University of Bucharest) · Marius Popescu (University of Bucharest)

跟随被干扰的领导者:平滑Minimax游戏的乐观和快速并行算法
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games
Arun Suggala (Carnegie Mellon University) · Praneeth Netrapalli (Microsoft Research)

深度变分实例细分
Deep Variational Instance Segmentation
Jialin Yuan (Oregon State University) · Chao Chen (Stony Brook University) · Fuxin Li (Oregon State University)

零膨胀泊松贝叶斯网络的贝叶斯因果结构学习
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks
Junsouk Choi (Texas A&M University) · Robert Chapkin (Texas A&M University) · Yang Ni (Texas A&M University)

关于证明对抗性例子具有鲁棒性的半定松弛的紧度
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples
Richard Zhang (UIUC)

使用合作游戏抽象评估和奖励团队合作
Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions
Tom Yan (Carnegie Mellon University) · Christian Kroer (Columbia University) · Alexander Peysakhovich (Facebook)

冰球的学习代理代表
Learning Agent Representations for Ice Hockey
Guiliang Liu (Simon Fraser University) · Oliver Schulte (Simon Fraser University) · Pascal Poupart (University of Waterloo & RBC Borealis AI) · Mike Rudd (University of Waterloo) · Mehrsan Javan (SPORTLOGiQ)

训练线性有限状态机
Training Linear Finite-State Machines
Arash Ardakani (McGill University) · Amir Ardakani (McGill University) · Warren Gross (McGill University)

用于医学成像的3D自我监督方法
3D Self-Supervised Methods for Medical Imaging
Aiham Taleb (Hasso-Plattner-Institute, Potsdam University) · Winfried Loetzsch (Hasso Plattner Institute) · Noel Danz (HPI) · Julius Severin (HPI) · Thomas Gaertner (HPI) · Benjamin Bergner (HPI) · Christoph Lippert (Hasso Plattner Institute for Digital Engineering, Universität Potsdam)

关于深度神经网络的神经质
On Numerosity of Deep Neural Networks
Xi Zhang (Shanghai Jiao Tong University) · Xiaolin Wu (McMaster University)

不可观测混杂下顺序决策的非政策性政策评估
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
Hongseok Namkoong (Stanford University) · Ramtin Keramati (Stanford University) · Steve Yadlowsky (Stanford University) · Emma Brunskill (Stanford University)

贝叶斯深度学习的Walsh-Hadamard变分推理
Walsh-Hadamard Variational Inference for Bayesian Deep Learning
Simone Rossi (EURECOM) · Sebastien Marmin (Department of Data Science, EURECOM) · Maurizio Filippone (EURECOM)

MCUNet:物联网设备上的微型深度学习
MCUNet: Tiny Deep Learning on IoT Devices
Ji Lin (MIT) · Wei-Ming Chen (National Taiwan University) · Yujun Lin (MIT) · john cohn (IBM Corp) · Chuang Gan (MIT-IBM Watson AI Lab) · Song Han (MIT)

消费者参考效应下的价格竞争中的无悔学习
No-regret Learning in Price Competitions under Consumer Reference Effects
Negin Golrezaei (Google Research) · Patrick Jaillet (MIT) · Jason Cheuk Nam N Liang (MIT)

避免点石成金:人工智能错位的后果
Avoiding the Midas Touch: Consequences of Misaligned AI
Simon Zhuang (UC Berkeley) · Dylan Hadfield-Menell (UC Berkeley)

Weisfeiler和Leman变得稀疏:迈向可扩展的高阶图嵌入
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Christopher Morris (Polytechnique Montreal) · Gaurav Rattan (RWTH Aachen University) · Petra Mutzel (University of Bonn)

基于组成能量模型的无监督联合k节点图表示
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models
Leonardo Cotta (Purdue University) · Carlos H. C. Teixeira (Universidade Federal de Minas Gerais) · Ananthram Swami (Army Research Laboratory, Adelphi) · Bruno Ribeiro (Purdue)

通过预筛查改善受政策约束的肾脏交换
Improving Policy-Constrained Kidney Exchange via Pre-Screening
Duncan McElfresh (University of Maryland) · Michael Curry (University of Maryland) · Tuomas Sandholm (CMU, Strategic Machine, Strategy Robot, Optimized Markets) · John Dickerson (University of Maryland)

通过广义下界Q学习进行自我模仿学习
Self-Imitation Learning via Generalized Lower Bound Q-learning
Yunhao Tang (Columbia University)

通过辅助变量局部探索学习基于离散能量的模型
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Hanjun Dai (Google Brain) · Rishabh Singh (Google Brain) · Bo Dai (Google Brain) · Charles Sutton (Google) · Dale Schuurmans (Google Brain & University of Alberta)

鲁棒模仿的魔术基准
The MAGICAL Benchmark for Robust Imitation
Sam Toyer (UC Berkeley) · Rohin Shah (UC Berkeley) · Andrew Critch (UC Berkeley) · Stuart Russell (UC Berkeley)

可控行为的弱监督强化学习
Weakly-Supervised Reinforcement Learning for Controllable Behavior
Lisa Lee (CMU / Google Brain / Stanford) · Ben Eysenbach (Carnegie Mellon University) · Russ Salakhutdinov (Carnegie Mellon University) · Shixiang (Shane) Gu (Google Brain) · Chelsea Finn (Stanford)

DNN图运算符设备放置的高效算法
Efficient Algorithms for Device Placement of DNN Graph Operators
Jakub Tarnawski (Microsoft Research) · Amar Phanishayee (Microsoft Research) · Nikhil Devanur (Amazon) · Divya Mahajan (Microsoft) · Fanny Nina Paravecino (Microsoft)

有条件随机优化的有偏随机梯度下降
Biased Stochastic Gradient Descent for Conditional Stochastic Optimization
Yifan Hu (University of Illinois at Urbana-Champaign) · Siqi Zhang (University of Illinois at Urbana-Champaign) · Xin Chen (University of Illinois at Urbana-Champaign) · Niao He (UIUC)

元学习中的建模与优化权衡
Modeling and Optimization Trade-off in Meta-learning
Katelyn Gao (Intel Labs) · Ozan Sener (Intel Labs)

深度神经网络的定向修剪
Directional Pruning of Deep Neural Networks
Shih-Kang Chao (University of Missouri) · Zhanyu Wang (Purdue University) · Yue Xing (Purdue University) · Guang Cheng (Purdue University)

有效的神经网络设计的结构化卷积
Structured Convolutions for Efficient Neural Network Design
Yash Bhalgat (Qualcomm AI Research) · Yizhe Zhang (Qualcomm AI Research) · Jamie Menjay Lin (Qualcomm AI Research) · Fatih Porikli (Qualcomm CR&D)

(减少方差)政策梯度和自然政策梯度方法的改进分析
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods
Yanli Liu (UCLA) · Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Tamer Basar (University of Illinois at Urbana-Champaign) · Wotao Yin (Alibaba US, DAMO Academy)

动量随机梯度下降的改进分析
An Improved Analysis of Stochastic Gradient Descent with Momentum
Yanli Liu (UCLA) · Yuan Gao (Columbia University) · Wotao Yin (Alibaba US, DAMO Academy)

PGM-Explainer:图形神经网络的概率图形模型说明
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
Minh N Vu (University of Florida) · My T. Thai (University of Florida)

端到端感觉运动学习的神经动力学策略
Neural Dynamic Policies for End-to-End Sensorimotor Learning
Shikhar Bahl (Carnegie Mellon University) · Mustafa Mukadam (Facebook AI Research) · Abhinav Gupta (Facebook AI Research/CMU) · Deepak Pathak (Carnegie Mellon University)

具有公共机会移动且超出此限制的两人扩展形式博弈中最佳相关均衡的多项式时间计算
Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond
Gabriele Farina (Carnegie Mellon University) · Tuomas Sandholm (CMU, Strategic Machine, Strategy Robot, Optimized Markets)

稀疏的图形记忆,可进行稳健的计划
Sparse Graphical Memory for Robust Planning
Misha Laskin (UC Berkeley) · Scott Emmons (UC Berkeley) · Ajay Jain (UC Berkeley) · Thanard Kurutach (University of California Berkeley) · Pieter Abbeel (UC Berkeley & covariant.ai) · Deepak Pathak (Carnegie Mellon University)

您的GAN秘密地是基于能量的模型,应该使用鉴别器驱动的潜在采样
Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling
Tong Che (MILA) · Ruixiang ZHANG (Mila/UdeM) · Jascha Sohl-Dickstein (Google Brain) · Hugo Larochelle (Google Brain) · Liam Paull (Université de Montréal) · Yuan Cao (Google Brain) · Yoshua Bengio (Mila / U. Montreal)

近似矩阵乘法时间的鲁棒高斯协方差估计
Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time
Jerry Li (Microsoft) · Guanghao Ye (University of Washington)

VIME:将自我和半监督学习的成功扩展到表格域
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
Jinsung Yoon (University of California, Los Angeles) · Yao Zhang (University of Cambridge) · James Jordon (University of Oxford) · Mihaela van der Schaar (University of Cambridge)

基于先验知识学习深度归因先验
Learning Deep Attribution Priors Based On Prior Knowledge
Ethan Weinberger (University of Washington) · Joseph Janizek (University of Washington) · Su-In Lee (University of Washington)

NVAE:深度层次变体自动编码器
NVAE: A Deep Hierarchical Variational Autoencoder
Arash Vahdat (NVIDIA) · Jan Kautz (NVIDIA)

MOReL:基于模型的离线强化学习
MOReL: Model-Based Offline Reinforcement Learning
Rahul Kidambi (Cornell University) · Aravind Rajeswaran (University of Washington) · Praneeth Netrapalli (Microsoft Research) · Thorsten Joachims (Cornell)

具有非线性函数逼近的Zap Q学习
Zap Q-Learning With Nonlinear Function Approximation
Shuhang Chen (University of Florida) · Adithya M Devraj (University of Florida) · Fan Lu (University of Florida) · Ana Busic (INRIA) · Sean Meyn (University of Florida)

相关鲁棒影响最大化
Correlation Robust Influence Maximization
Louis Chen (Naval Postgraduate School) · Divya Padmanabhan (Singapore University of Technology and Design) · Chee Chin Lim (Singapore University of Technology and Design) · Karthik Natarajan (Singapore University of Technology and Design)

条件元学习的结构化预测
Structured Prediction for Conditional Meta-Learning
Ruohan Wang (Imperial College London) · Yiannis Demiris (Imperial College London) · Carlo Ciliberto (Imperial College London)

隐私保护协作机器学习的可扩展方法
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
Jinhyun So (University of Southern California) · Basak Guler (University of Southern California) · Salman Avestimehr (University of Southern California)

具有一般目标功能的计划:超越总奖励
Planning with General Objective Functions: Going Beyond Total Rewards
Ruosong Wang (Carnegie Mellon University) · Peilin Zhong (Columbia University) · Simon Du (Institute for Advanced Study) · Russ Salakhutdinov (Carnegie Mellon University) · Lin Yang (UCLA)

远距RL比短距RL更难吗?
Is Long Horizon RL More Difficult Than Short Horizon RL?
Ruosong Wang (Carnegie Mellon University) · Simon Du (Institute for Advanced Study) · Lin Yang (UCLA) · Sham Kakade (University of Washington & Microsoft Research)

具有通用值函数逼近的强化学习:通过有界Eluder维进行有效证明的方法
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension
Ruosong Wang (Carnegie Mellon University) · Russ Salakhutdinov (Carnegie Mellon University) · Lin Yang (UCLA)

GCN与GPU相遇:将“何时采样”与“如何采样”分离
GCN meets GPU: Decoupling “When to Sample” from “How to Sample”
Morteza Ramezani (Pennsylvania State University) · Weilin Cong (Pennsylvania State University) · Mehrdad Mahdavi (Pennsylvania State University) · Anand Sivasubramaniam (Penn State) · Mahmut Kandemir (Pennsylvania State University)

隐式图神经网络
Implicit Graph Neural Networks
Fangda Gu (UC Berkeley) · Heng Chang (Tsinghua University) · Wenwu Zhu (Tsinghua University) · Somayeh Sojoudi (University of California, Berkeley) · Laurent El Ghaoui (UC Berkeley)

风险敏感型学习的学习范围
Learning Bounds for Risk-sensitive Learning
Jaeho Lee (KAIST) · Sejun Park (KAIST) · Jinwoo Shin (KAIST)

共享空间转移学习,用于分析多部位fMRI数据
Shared Space Transfer Learning for analyzing multi-site fMRI data
Muhammad Yousefnezhad (University of Alberta) · Alessandro Selvitella (Purdue University Fort Wayne) · Daoqiang Zhang (Nanjing University of Aeronautics and Astronautics) · Andrew Greenshaw (University of Alberta) · Russell Greiner (University of Alberta)

寻求与问题相关的最佳学习率
Towards Problem-dependent Optimal Learning Rates
Yunbei Xu (Columbia University) · Assaf Zeevi (Columbia University)

从比赛中估算技能分布
Estimation of Skill Distribution from a Tournament
Ali Jadbabaie (MIT) · Anuran Makur (MIT) · Devavrat Shah (Massachusetts Institute of Technology)

分布式学习的选举编码:保护SignSGD免受拜占庭式攻击
Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks
Jy-yong Sohn (KAIST) · Dong-Jun Han (KAIST) · Beongjun Choi (KAIST) · Jaekyun Moon (Korea Advanced Institute of Science and Technology)

安全和Seldonian强化学习算法的安全性分析
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms
Pinar Ozisik (UMass Amherst) · Philip Thomas (University of Massachusetts Amherst)

可靠的度量学习
Provably Robust Metric Learning
Lu Wang (Nanjing University) · Xuanqing Liu (University of California, Los Angeles) · Jinfeng Yi (JD Research) · Yuan Jiang (National Key lab for Novel Software Technology) · Cho-Jui Hsieh (UCLA)

深度神经网络的超低精度4位训练
Ultra-Low Precision 4-bit Training of Deep Neural Networks
Xiao Sun (IBM Thomas J. Watson Research Center) · Naigang Wang (IBM T. J. Watson Research Center) · Chia-Yu Chen (IBM research) · Jiamin Ni (IBM) · Ankur Agrawal (IBM Research) · Xiaodong Cui (IBM T. J. Watson Research Center) · Swagath Venkataramani (IBM Research) · Kaoutar El Maghraoui (IBM Research) · Vijayalakshmi (Viji) Srinivasan (IBM TJ Watson) · Kailash Gopalakrishnan (IBM Research)

感官和敏感性分析:由于未观察到的混淆而对偏差的简单事后分析
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
Victor Veitch (Columbia University) · Anisha Zaveri (Weill Cornell Medicine)

GNNGuard:防御图神经网络对抗攻击
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
Xiang Zhang (Harvard University) · Marinka Zitnik (Harvard University)

一种解决方案不是您所需要的:通过结构化MaxEnt RL进行少量外推
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL
Saurabh Kumar (Stanford University) · Aviral Kumar (UC Berkeley) · Sergey Levine (UC Berkeley) · Chelsea Finn (Stanford)

立体视差估计的Wasserstein距离
Wasserstein Distances for Stereo Disparity Estimation
Divyansh Garg (Cornell University) · Yan Wang (Cornell) · Bharath Hariharan (Cornell University) · Mark Campbell (Cornell University) · Kilian Weinberger (Cornell University / ASAPP Research) · Wei-Lun Chao (Ohio State University (OSU))

拉伸混合物的有效聚类:景观和最优性
Efficient Clustering for Stretched Mixtures: Landscape and Optimality
Kaizheng Wang (Columbia University) · Yuling Yan (Princeton University) · Mateo Diaz (Cornell University)

3D点云的旋转不变局部到全局表示学习
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud
SEOHYUN KIM (Seoul National University) · JaeYoo Park (Seoul National University) · Bohyung Han (Seoul National University)

转向具有可微分组归一化的更深图神经网络
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Kaixiong Zhou (Texas A&M University) · Xiao Huang (The Hong Kong Polytechnic University) · Yuening Li (Texas A&M University) · Daochen Zha (Texas A&M University) · Rui Chen (Samsung Research America) · Xia Hu (Texas A&M University)

通过增强明智的体重共享改善自动增强
Improving Auto-Augment via Augmentation-Wise Weight Sharing
Keyu Tian (Sensetime; Beihang University) · CHEN LIN (SenseTime) · Ming Sun (SenseTime Group Limited) · Luping Zhou (University of Sydney) · Junjie Yan (Sensetime Group Limited) · Wanli Ouyang (The University of Sydney)

旋转二进制神经网络
Rotated Binary Neural Network
Mingbao Lin (Xiamen University) · Rongrong Ji (Xiamen University, China) · Zihan Xu (Xiamen University, China) · Baochang Zhang (Beihang University) · Yan Wang (Pinterest) · Yongjian Wu (Tencent Technology (Shanghai) Co.,Ltd) · Feiyue Huang (Tencent) · Chia-Wen Lin (National Tsing Hua University)

拟线性近半线性时间上亚模最大化的确定性逼近
Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time
Kai Han (University of Science and Technology of China) · zongmai Cao (University of Science and Technology of China) · Shuang Cui (University of Science and Technology of China) · Benwei Wu (University of Science and Technology of China)

网络大小和两层神经网络记忆权重的大小
Network size and size of the weights in memorization with two-layers neural networks
Sebastien Bubeck (Microsoft Research) · Ronen Eldan (Weizmann) · Yin Tat Lee (UW) · Dan Mikulincer (Institute Weizmann)

双向机器人操纵的深度模仿学习
Deep Imitation Learning for Bimanual Robotic Manipulation
Fan Xie (Northeastern University) · Alexander Chowdhury (Northeastern University) · M. Clara De Paolis Kaluza (Northeastern University) · Linfeng Zhao (Northeastern University) · Lawson Wong (Northeastern University) · Rose Yu (University of California, San Diego)

学习突变与超梯度引导人群
Learning to Mutate with Hypergradient Guided Population
Zhiqiang Tao (Santa Clara University) · Yaliang Li (Alibaba Group) · Bolin Ding (“Data Analytics and Intelligence Lab, Alibaba Group”) · Ce Zhang (ETH Zurich) · Jingren Zhou (Alibaba Group) · Yun Fu (Northeastern University)

TriHard损失的困境和元素加权的TriHard损失以重新识别人
The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification
Yihao Lv (Zhejiang University) · Youzhi Gu (Zhejiang University) · Liu Xinggao (Zhejiang University)

次模块元学习
Submodular Meta-Learning
Arman Adibi (University of Pennsylvania) · Aryan Mokhtari (UT Austin) · Hamed Hassani (UPenn)

COPT:图上的最优协调运输
COPT: Coordinated Optimal Transport on Graphs
Yihe Dong (Microsoft) · Will Sawin (Columbia University)

少即是多:使用少量代理的深度图度量学习视角
Less is More: A Deep Graph Metric Learning Perspective Using Few Proxies
Yuehua Zhu (Xidian University) · Muli Yang (Xidian University) · Cheng Deng (Xidian University) · Wei Liu (Tencent AI Lab)

研究半监督视频对象分割中的循环机制
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
Yuxi Li (Shanghai Jiao Tong University) · Jinlong Peng (Tencent Youtu Lab) · Ning Xu (Adobe Research) · John See (Multimedia University) · Weiyao Lin (Shanghai Jiao Tong university)

高通量同步深度RL
High-Throughput Synchronous Deep RL
Iou-Jen Liu (University of Illinois at Urbana-Champaign) · Raymond Yeh (University of Illinois at Urbana–Champaign) · Alexander Schwing (University of Illinois at Urbana-Champaign)

通过反馈和局部可塑性学习
Learning to Learn with Feedback and Local Plasticity
Jack Lindsey (Columbia University) · Ashok Litwin-Kumar (Columbia University)

用于表达概率分布的深层神经网络的通用逼近定理
A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions
Yulong Lu (Duke University) · Jianfeng Lu (Duke University)

用于评估安全关键自治系统的神经桥采样
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems
Aman Sinha (Stanford University) · Matthew O’Kelly (University of Pennsylvania) · Russ Tedrake (MIT) · John Duchi (Stanford)

有效覆盖范围和自适应覆盖范围的分类
Classification with Valid and Adaptive Coverage
Yaniv Romano (Stanford University) · Matteo Sesia (Stanford) · Emmanuel Candes (Stanford University)

学习稀疏原型以生成文本
Learning Sparse Prototypes for Text Generation
Junxian He (Carnegie Mellon University) · Taylor Berg-Kirkpatrick (University of California San Diego) · Graham Neubig (Carnegie Mellon University)

走向有界记忆学习的组合表征
Towards a Combinatorial Characterization of Bounded-Memory Learning
Alon Gonen (UCSD) · Shachar Lovett (University of California San Diego) · Michal Moshkovitz (University of California San Diego)

通过拓扑分析从神经网络中检测相互作用
Detecting Interactions from Neural Networks via Topological Analysis
Zirui Liu (Texas A&M University) · Qingquan Song (Texas A&M University) · Kaixiong Zhou (Texas A&M University) · Ting-Hsiang Wang (Texas A&M University) · Ying Shan (Tencent) · Xia Hu (Texas A&M University)

通过重新采样敏感属性来实现均等赔率
Achieving Equalized Odds by Resampling Sensitive Attributes
Yaniv Romano (Stanford University) · Stephen Bates (Stanford University) · Emmanuel Candes (Stanford University)

SoftFlow:规范流形上的流的概率框架
SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds
Hyeongju Kim (Seoul National University) · Hyeonseung Lee (Seoul National University) · Woo Hyun Kang (Seoul National University) · Joun Yeop Lee (Seoul National University) · Nam Soo Kim (Seoul National University)

RepPoints v2:验证符合回归以进行对象检测
RepPoints v2: Verification Meets Regression for Object Detection
Yihong Chen (Peking University) · Zheng Zhang (MSRA) · Yue Cao (Microsoft Research) · Liwei Wang (Peking University) · Stephen Lin (Microsoft Research) · Han Hu (Microsoft Research Asia)

特征问题迭代方法的入门收敛
Entrywise convergence of iterative methods for eigenproblems
Vasileios Charisopoulos (Cornell University) · Austin Benson (Cornell University) · Anil Damle (Cornell University)

学习策略感知线性分类器
Learning Strategy-Aware Linear Classifiers
Yiling Chen (Harvard University) · Yang Liu (UC Santa Cruz) · Chara Podimata (Harvard University)

表示学习的功能正则化:统一的理论视角
Functional Regularization for Representation Learning: A Unified Theoretical Perspective
Siddhant Garg (University of Wisconsin-Madison) · Yingyu Liang (University of Wisconsin Madison)

通过变压器指导的程序综合学习在多智能体系统中进行通信
Learning to Communicate in Multi-Agent Systems via Transformer-Guided Program Synthesis
Jeevana Priya Inala (MIT) · Yichen Yang (MIT) · James Paulos (University of Pennsylvania) · Yewen Pu (MIT) · Osbert Bastani (University of Pennysylvania) · Vijay Kumar (University of Pennsylvania) · Martin Rinard (MIT) · Armando Solar-Lezama (MIT)

在光滑非凸线性约束优化问题中有效地找到二阶平稳点
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems
Songtao Lu (IBM Research) · Meisam Razaviyayn (University of Southern California) · Bo Yang (University of Minnesota) · Kejun Huang (University of Florida) · Mingyi Hong (University of Minnesota)

结构方程模型的有效神经估计:一种对抗方法
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach
Luofeng Liao (University of Chicago) · You-Lin Chen (Department of Statistics, University of Chicago) · Zhuoran Yang (Princeton) · Bo Dai (Google Brain) · Mladen Kolar (University of Chicago) · Zhaoran Wang (Northwestern University)

用于神经网络去偏后的事后方法研究
A Study on Post-Hoc Methods for Debiasing Neural Networks
Yash Savani (RealityEngines.AI) · Colin White (RealityEngines.AI) · Naveen Sundar Govindarajulu (RealityEngines.AI)

通过深度神经网络进行多保真贝叶斯优化
Multi-Fidelity Bayesian Optimization via Deep Neural Networks
Shibo Li (University of Utah) · Wei Xing (University of Utah) · Robert Kirby (University of Utah) · Shandian Zhe (University of Utah)

使用信息值在POMDP中依赖信念的宏动作发现
Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information
Genevieve E Flaspohler (Massachusetts Institute of Technology) · Nicholas Roy (MIT) · John W Fisher III (MIT)

半监督神经架构搜索
Semi-Supervised Neural Architecture Search
Renqian Luo (University of Science and Technology of China) · Xu Tan (Microsoft Research) · Rui Wang (Microsoft Research Asia) · Tao Qin (Microsoft Research) · Enhong Chen (University of Science and Technology of China) · Tie-Yan Liu (Microsoft Research Asia)

路由游戏中的混乱之路:无政府状态的价格何时变得过于乐观?
The route to chaos in routing games: When is price of anarchy too optimistic?
Thiparat Chotibut (Chulalongkorn university) · Fryderyk Falniowski (Cracow University of Economics) · Michał Misiurewicz (Indiana University-Purdue University Indianapolis) · Georgios Piliouras (Singapore University of Technology and Design)

基于情景记忆的终身学习算法的改进方案
Improved Schemes for Episodic Memory based Lifelong Learning Algorithm
Yunhui Guo (University of California, San Diego) · Mingrui Liu (Boston University) · Tianbao Yang (The University of Iowa) · Tajana Rosing (University of California, San Diego)

可以转移多样性:针对白盒和黑盒攻击的输出多样化
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks
Yusuke Tashiro (Japan Digital Design) · Yang Song (Stanford University) · Stefano Ermon (Stanford)

自动学习注意
Auto Learning Attention
Benteng Ma (Northwestern Polytechnical University) · Jing Zhang (The University of Sydney) · Yong Xia (Northwestern Polytechnical University, Research & Development Institute of Northwestern Polytechnical University in Shenzhen) · Dacheng Tao (University of Sydney)

随机斯坦因差异
Stochastic Stein Discrepancies
Jackson Gorham (Stanford University) · Anant Raj (Max Planck Institute for Intelligent Systems) · Lester Mackey (Microsoft Research)

主动学习线性分类器的比较能力
The Power of Comparisons for Actively Learning Linear Classifiers
Max Hopkins (University of California San Diego) · Daniel Kane (UCSD) · Shachar Lovett (University of California San Diego)

自适应距离估计
On Adaptive Distance Estimation
Yeshwanth Cherapanamjeri (UC Berkeley) · Jelani Nelson (UC Berkeley)

对悲观主义的美满乐观:超越渐进最优性的结构强盗
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality
Kwang-Sung Jun (U of Arizona) · Chicheng Zhang (University of Arizona)

具有异构属性空间的任务的元学习
Meta-learning from Tasks with Heterogeneous Attribute Spaces
Tomoharu Iwata (NTT) · Atsutoshi Kumagai (NTT Software Innovation Center)

自蒸馏作为特定于实例的标签平滑
Self-Distillation as Instance-Specific Label Smoothing
Zhilu Zhang (Cornell University) · Mert Sabuncu (Cornell)

安全随机凸优化的最优查询复杂度
Optimal Query Complexity of Secure Stochastic Convex Optimization
Wei Tang (Washington University in St.Louis) · Chien-Ju Ho (Washington University in St. Louis) · Yang Liu (UC Santa Cruz)

MomentumRNN:将动量整合到递归神经网络中
MomentumRNN: Integrating Momentum into Recurrent Neural Networks
Tan Nguyen (Rice University/UCLA) · Richard Baraniuk (Rice University) · Andrea Bertozzi (UCLA) · Stanley Osher (UCLA) · Bao Wang (UCLA)

多重播放和马尔可夫奖赏的最优自适应分配的循环Kullback-Leibler上置信区间的有限时间分析
Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards
Vrettos Moulos (UC Berkeley)

贝叶斯多类型均值场多智能体模仿学习
Bayesian Multi-type Mean Field Multi-agent Imitation Learning
Fan Yang (University at Buffalo) · Alina Vereshchaka (University at Buffalo) · Changyou Chen (University at Buffalo) · Wen Dong (University at Buffalo)

没有对话框数据的对话框:从VQA数据学习可视对话框代理
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data
Michael Cogswell (Georgia Tech) · Jiasen Lu (Allen Institute of Artificial Intelligence ) · Rishabh Jain (Georgia Tech) · Stefan Lee (Oregon State University) · Devi Parikh (Georgia Tech / Facebook AI Research (FAIR)) · Dhruv Batra (Georgia Tech / Facebook AI Research (FAIR))

分层粒度转移学习
Hierarchical Granularity Transfer Learning
Shaobo Min (USTC) · Hongtao Xie (University of Science and Technology of China) · Hantao Yao ( Institute of Automation, Chinese Academy of Sciences) · Xuran Deng (University of Science and Technology of China) · Zheng-Jun Zha (University of Science and Technology of China) · Yongdong Zhang (University of Science and Technology of China)

离散图形模型中变化推理的概率电路
Probabilistic Circuits for Variational Inference in Discrete Graphical Models
Andy Shih (Stanford University) · Stefano Ermon (Stanford)

在模型错误规范下学习:变分和集成方法的应用
Learning under Model Misspecification: Applications to Variational and Ensemble methods
Andres Masegosa (University of Almeria)

自我训练可避免在域移位下使用虚假特征
Self-training Avoids Using Spurious Features Under Domain Shift
Yining Chen (Stanford University) · Colin Wei (Stanford University) · Ananya Kumar (Stanford University) · Tengyu Ma (Stanford University)

具有内存和竞争控制功能的在线优化
Online Optimization with Memory and Competitive Control
Guanya Shi (Caltech) · Yiheng Lin (California Institute of Technology) · Soon-Jo Chung (Caltech) · Yisong Yue (Caltech) · Adam Wierman (California Institute of Technology)

Glow-TTS:通过单调对齐搜索生成文本到语音的生成流
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search
Jaehyeon Kim (Kakao Enterprise) · Sungwon Kim (Seoul National University) · Jungil Kong (Kakao Enterprise) · Sungroh Yoon (Seoul National University)

自动学习用于优化问题的紧凑型质量意识代理
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
Kai Wang (Harvard University) · Bryan Wilder (Harvard University) · Andrew Perrault (Harvard University) · Milind Tambe (Harvard University/Google)

参数模态回归的隐式函数学习方法
An implicit function learning approach for parametric modal regression
Yangchen Pan (University of Alberta) · Ehsan Imani (University of Alberta) · Martha White (University of Alberta) · Amir-massoud Farahmand (Vector Institute and University of Toronto)

基于模型的对抗性元强化学习
Model-based Adversarial Meta-Reinforcement Learning
Zichuan Lin (Tsinghua University) · Garrett W. Thomas (Stanford University) · Guangwen Yang (Tsinghua University) · Tengyu Ma (Stanford University)

Falcon:对加密数据的快速光谱推断
Falcon: Fast Spectral Inference on Encrypted Data
Qian Lou (Indiana University Bloomington) · Wen-jie Lu (Alibaba Group) · Cheng Hong (Alibaba Group) · Lei Jiang (Indiana University Bloomington)

基于示例的生成和数据增强的示例VAE
Exemplar VAEs for Exemplar based Generation and Data Augmentation
Sajad Norouzi (University of Toronto / Vector Institute) · David J Fleet (University of Toronto) · Mohammad Norouzi (Google Brain)

高维感知器中的泛化误差:采用凸优化方法逼近贝叶斯误差
Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization
Benjamin Aubin (Ipht Saclay) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL) · Yue Lu (Harvard University) · Lenka Zdeborová (University Paris-Saclay & EPFL)

二进制整数和在线线性规划的简单快速算法
Simple and Fast Algorithm for Binary Integer and Online Linear Programming
Xiaocheng Li (Department of Management Science and Engineering, Stanford University) · Chunlin Sun (Stanford University) · Yinyu Ye (Standord)

搜索递归体系结构以基于路径的知识图嵌入
Searching Recurrent Architecture for Path-based Knowledge Graph Embedding
Yongqi Zhang (4Paradigm Inc.) · Quanming Yao (4paradigm) · Lei Chen (Hong Kong University of Science and Technology)

可有效地用于非政策学习的神经GTD
Provably Efficient Neural GTD for Off-Policy Learning
Hoi-To Wai (The Chinese University of Hong Kong) · Zhuoran Yang (Princeton) · Zhaoran Wang (Northwestern University) · Mingyi Hong (University of Minnesota)

不可微函数自动微分的正确性
On Correctness of Automatic Differentiation for Non-Differentiable Functions
Wonyeol Lee (KAIST) · Hangyeol Yu (KAIST) · Xavier Rival (ENS) · Hongseok Yang (KAIST)

中点随机抽样方法的遍历性,偏差和渐近正态性
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
Ye He (University of California, Davis) · Krishnakumar Balasubramanian (University of California, Davis) · Murat Erdogdu (University of Toronto)

MuSCLE:使用深熵模型的LiDAR多次扫描压缩
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
Sourav Biswas (University of Waterloo) · Jerry Liu (Uber ATG) · Kelvin Wong (University of Toronto) · Shenlong Wang (University of Toronto) · Raquel Urtasun (Uber ATG)

图上的泛函逼近
Universal Function Approximation on Graphs
Rickard Gabrielsson (Stanford University)

稀疏半空间的有效主动学习和任意有界噪声
Efficient active learning of sparse halfspaces with arbitrary bounded noise
Chicheng Zhang (University of Arizona) · Jie Shen (Stevens Institute of Technology) · Pranjal Awasthi (Rutgers University/Google)

城堡:通过辅助因果图发现进行正则化
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Trent Kyono (UCLA) · Yao Zhang (University of Cambridge) · Mihaela van der Schaar (University of Cambridge)

减少离散量支持的随机算法
A Randomized Algorithm to Reduce the Support of Discrete Measures
Francesco Cosentino (University of Oxford) · Harald Oberhauser (University of Oxford) · Alessandro Abate (University of Oxford)

随机非凸-强凹极小极大问题的随机递归梯度下降上升
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems
Luo Luo (The Hong Kong University of Science and Technology) · Haishan Ye (The Chinese University of Hong Kong, Shenzen) · Zhichao Huang (HKUST) · Tong Zhang (Tencent AI Lab)

贝叶斯滤波将自适应和非自适应神经网络优化方法统一起来
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods
Laurence Aitchison (University of Cambridge)

从总体观察中学习
Learning from Aggregate Observations
Yivan Zhang (The University of Tokyo / RIKEN) · Nontawat Charoenphakdee (The University of Tokyo / RIKEN) · Zhenguo Wu (The University of Tokyo) · Masashi Sugiyama (RIKEN / University of Tokyo)

基于模型优化的模型反演网络
Model Inversion Networks for Model-Based Optimization
Aviral Kumar (UC Berkeley) · Sergey Levine (UC Berkeley)

预测与专家建议已损坏
Prediction with Corrupted Expert Advice
Idan Amir (Tel-Aviv University) · Idan Attias (Ben Gurion University) · Tomer Koren (Google) · Yishay Mansour (Tel Aviv University / Google) · Roi Livni (Tel Aviv University)

通过课程归纳进行安全强化学习
Safe Reinforcement Learning via Curriculum Induction
Matteo Turchetta (ETH Zurich) · Andrey Kolobov (Microsoft Research) · Shital Shah (Microsoft) · Andreas Krause (ETH Zurich) · Alekh Agarwal (Microsoft Research)

量化经验Wasserstein距离到一组量度:克服维数的诅咒
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
Nian Si (Stanford University) · Soumyadip Ghosh (IBM Research) · Jose Blanchet (Stanford University) · Mark Squillante (IBM Research)

元合并以继续学习
Meta-Consolidation for Continual Learning
Joseph K J (Indian Institute of Technology Hyderabad) · Vineeth Nallure Balasubramanian (Indian Institute of Technology, Hyderabad)

离线强化学习的保守Q学习
Conservative Q-Learning for Offline Reinforcement Learning
Aviral Kumar (UC Berkeley) · Aurick Zhou (University of California, Berkeley) · George Tucker (Google Brain) · Sergey Levine (UC Berkeley)

隐式照明和材质的多视图神经表面重构
Multiview Neural Surface Reconstruction with Implicit Lighting and Material
Lior Yariv (Weizmann Institute of Science) · Yoni Kasten (Weizmann Institute) · Dror Moran (Weizmann Institute of Science) · Meirav Galun (Weizmann Institute of Science) · Matan Atzmon (Weizmann Institute Of Science) · Basri Ronen (Weizmann Inst.) · Yaron Lipman (Weizmann Institute of Science)

Langevin动力学在歧管上的快速收敛:测地线与Log-Sobolev相遇
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev
Xiao Wang (Singapore university of technology and design) · Qi Lei (Princeton University) · Ioannis Panageas (UC Irvine)

HiFi-GAN:高效,高保真语音合成的生成对抗网络
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
Jungil Kong (Kakao Enterprise) · Jaehyeon Kim (Kakao Enterprise) · Jaekyoung Bae (Kakao Enterprise)

分散式深度学习中的实用低排位通信压缩
Practical Low-Rank Communication Compression in Decentralized Deep Learning
Thijs Vogels (EPFL) · Sai Praneeth Karimireddy (EPFL) · Martin Jaggi (EPFL)

平稳一致的概率回归树
Smooth And Consistent Probabilistic Regression Trees
Sami Alkhoury (University Grenoble Alpes) · Emilie Devijver (CNRS - UGA) · Marianne Clausel (IECL) · Myriam Tami (Université Paris-Saclay) · Eric Gaussier (Université Joseph Fourier, Grenoble) · georges Oppenheim (Private)

SAC:通过稀疏的自适应连接来加速和构建自我注意
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection
Xiaoya Li (Shannon.AI) · Yuxian Meng (Shannon.AI) · Mingxin Zhou (Shannon.AI) · Qinghong Han (Shannon.AI) · Fei Wu (Zhejiang University) · Jiwei Li (Shannon.AI)

线性解缠结表示和无监督动作估计
Linear Disentangled Representations and Unsupervised Action Estimation
Matthew Painter (University of Southampton) · Adam Prugel-Bennett (apb@ecs.soton.ac.uk) · Jonathon Hare (University of Southampton)

简化和稳健负采样以进行隐式协作过滤
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering
Jingtao Ding (Tsinghua University) · Yuhan Quan (Tsinghua University) · Quanming Yao (4paradigm) · Yong Li (Tsinghua University) · Depeng Jin (Tsinghua University)

介入式少儿学习
Interventional Few-Shot Learning
Zhongqi Yue (Nanyang Technological University) · Hanwang Zhang (NTU) · Qianru Sun (Singapore Management University) · Xian-Sheng Hua (Damo Academy, Alibaba Group)

穗状和板状变分贝叶斯用于高维逻辑回归
Spike and slab variational Bayes for high dimensional logistic regression
Kolyan Ray (Imperial College London) · Botond Szabo (Leiden University) · Gabriel Clara (Vrije Universiteit Amsterdam)

个性化联合学习的下界和最优算法
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Filip Hanzely (KAUST) · Slavomír Hanzely (KAUST) · Samuel Horváth (King Abdullah University of Science and Technology) · Peter Richtarik (KAUST)

BRP-NAS:使用GCN的基于预测的NAS
BRP-NAS: Prediction-based NAS using GCNs
Thomas Chau (Samsung AI Center Cambridge) · Lukasz Dudziak (Samsung AI Center Cambridge) · Mohamed Abdelfattah (Samsung AI Centre Cambridge) · Royson Lee (Samsung AI Center Cambridge) · Hyeji Kim (Samsung AI Center Cambridge) · Nicholas Lane (Samsung AI Center Cambridge & University of Oxford)

通过图泊松伽玛信仰网络进行深度关系主题建模
Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network
Chaojie Wang (Xidian University) · Hao Zhang (Xidian University) · Bo Chen (Xidian University) · Dongsheng Wang (Xidian University) · Zhengjue Wang (Xidian University) · Mingyuan Zhou (University of Texas at Austin)

自动全景:用于全景分割的协作式多组件体系结构搜索
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation
Yangxin Wu (Sun Yat-sen University) · Gengwei Zhang (Sun Yat-sen University) · Hang Xu (Huawei Noah’s Ark Lab) · Xiaodan Liang (Sun Yat-sen University) · Liang Lin (Sun Yat-Sen University)

具有噪声可能性的变分贝叶斯蒙特卡洛
Variational Bayesian Monte Carlo with Noisy Likelihoods
Luigi Acerbi (University of Helsinki)

超级损失:健壮的课程学习的普遍损失
SuperLoss: A Generic Loss for Robust Curriculum Learning
Thibault Castells (Naver Labs) · Philippe Weinzaepfel (NAVER LABS Europe) · Jerome Revaud (Naver Labs Europe)

基于K均值和比率削减的统一视图的高效聚类
Efficient Clustering Based On A Unified View Of K-means And Ratio-cut
Shenfei Pei (Northwestern Polytechnical University) · Feiping Nie (University of Texas Arlington) · Rong Wang (Northwestern Polytechnical University) · Xuelong Li (Northwestern Polytechnical University)

储层计算满足递归核和结构化变换
Reservoir Computing meets Recurrent Kernels and Structured Transforms
Jonathan Dong (Laboratoire Kastler-Brossel) · Ruben Ohana (Ecole Normale Supérieure & LightOn) · Mushegh Rafayelyan (Kastler-Brossel Laboratory (ENS, Sorbonne U., PSL U., CNRS, Collège de France)) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL)

努力使域内和分布外示例之间的表示差距最大化
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples
Jay Nandy (National University of Singapore) · Wynne Hsu (National University of Singapore) · Mong Li Lee (National University of Singapore)

发现,幻觉和适应:用于语义分割的开放复合域适应
Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation
KwanYong Park (KAIST) · Sanghyun Woo (KAIST) · Inkyu Shin (Korea Advanced Institute of Science and Technology) · In So Kweon (KAIST)

组合半强子汤普森抽样的统计效率
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits
Pierre Perrault (INRIA - ENS Paris Saclay) · Etienne Boursier (ENS Paris Saclay) · Michal Valko (DeepMind) · Vianney Perchet (ENSAE & Criteo AI Lab)

通过深核进行少拍环境的贝叶斯元学习
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Massimiliano Patacchiola (University of Edinburgh) · Jack Turner (University of Edinburgh) · Elliot J. Crowley (University of Edinburgh) · Michael O’Boyle (University of Edinburgh) · Amos Storkey (University of Edinburgh)

私人学习半空间:简化构造并降低样本复杂度
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
Haim Kaplan (TAU, GOOGLE) · Yishay Mansour (Tel Aviv University / Google) · Uri Stemmer (Ben-Gurion University) · Eliad Tsfadia (Tel Aviv University and Google)

目标传播的理论框架
A Theoretical Framework for Target Propagation
Alexander Meulemans (ETH Zürich | University of Zürich | Institute of Neuroinformatics) · Francesco Carzaniga (Institute of Neuroinformatics, University of Zurich and ETH Zurich) · Johan Suykens (KU Leuven) · João Sacramento (ETH Zurich) · Benjamin F. Grewe (ETH Zurich)

深壳:无监督形状与最佳运输的对应
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport
Marvin Eisenberger (Technical University of Munich) · Aysim Toker (TUM) · Laura Leal-Taixé (TUM) · Daniel Cremers (Technical University of Munich)

局部相关性对学习某些深层功能的影响
The Implications of Local Correlation on Learning Some Deep Functions
Eran Malach (Hebrew University Jerusalem Israel) · Shai Shalev-Shwartz (Mobileye & HUJI)

无噪声线性模型下随机梯度下降的紧非参数收敛速度
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model
Raphaël Berthier (INRIA, ENS) · Francis Bach (INRIA - Ecole Normale Superieure) · Pierre Gaillard ()

自回归分数匹配
Autoregressive Score Matching
Chenlin Meng (Stanford University) · Lantao Yu (Stanford University) · Yang Song (Stanford University) · Jiaming Song (Stanford University) · Stefano Ermon (Stanford)

Wasserstein-近似高斯过程的分位数传播
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
Rui Zhang (The Australian National University) · Christian Walder (DATA61) · Edwin Bonilla (Data61) · Marian-Andrei Rizoiu (University of Technology Sydney) · Lexing Xie (Australian National University)

关于深度学习的普遍性
On the universality of deep learning
Emmanuel Abbe (Princeton University) · Colin Sandon (MIT)

迭代均质神经网络对尺度不变图相关问题的解决
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs
Hao Tang (Shanghai Jiao Tong University) · Zhiao Huang (University of California San Diego) · Jiayuan Gu (University of California, San Diego) · Bao-Liang Lu (Shanghai Jiao Tong University) · Hao Su (UCSD)

通过神经网络学习奇偶校验
Learning Parities with Neural Networks
Eran Malach (Hebrew University Jerusalem Israel) · Amit Daniely (Hebrew University and Google Research)

神经架构生成器优化
Neural Architecture Generator Optimization
Robin Ru (Oxford University) · Pedro M Esperança (Huawei Noah’s Ark Lab, London) · Fabio Maria Carlucci (Huawei Noah’s Ark Lab)

时间反向对称ODE网络
Time-Reversal Symmetric ODE Network
In Huh (Samsung Electronics, Samsung Advanced Institute of Technology) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics) · Sung Ju Hwang (KAIST, AITRICS) · Jinwoo Shin (KAIST)

CoADNet:用于共凸目标检测的协作聚合和分布网络
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection
Qijian Zhang (City University of Hong Kong) · Runmin Cong (Beijing Jiaotong University) · Junhui Hou (City University of Hong Kong, Hong Kong) · Chongyi Li ( Nanyang Technological University) · Yao Zhao (Beijing Jiaotong University)

隐性偏差可以解释泛化吗?随机凸优化的案例研究
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study
Assaf Dauber (Tel-Aviv University) · Meir Feder (Tel-Aviv University) · Tomer Koren (Google) · Roi Livni (Tel Aviv University)

蒙克豪森强化学习
Munchausen Reinforcement Learning
Nino Vieillard (Google Brain) · Olivier Pietquin (Google Research Brain Team) · Matthieu Geist (Google Brain)

通过令人难忘的过去的功能正规化进行持续深度学习
Continual Deep Learning by Functional Regularisation of Memorable Past
Pingbo Pan (University of Technology Sydney) · Siddharth Swaroop (University of Cambridge) · Alexander Immer (EPFL) · Runa Eschenhagen (University of Osnabrueck) · Richard E Turner (University of Cambridge) · Mohammad Emtiyaz Khan (RIKEN, Tokyo)

扰动梯度跟踪的分散非凸优化中的二阶最优性
Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking
Isidoros Tziotis (UT Austin) · Constantine Caramanis (UT Austin) · Aryan Mokhtari (UT Austin)

随机森林中的关节
Joints in Random Forests
Alvaro Correia (Eindhoven University of Technology) · Robert Peharz (University of Cambridge) · Cassio de Campos (Eindhoven University of Technology)

分层神经体系结构搜索以实现深度立体匹配
Hierarchical Neural Architecture Search for Deep Stereo Matching
Xuelian Cheng (Monash University) · Yiran Zhong (Australian National University) · Mehrtash T Harandi (Monash University) · Yuchao Dai (Northwestern Polytechnical University) · Xiaojun Chang (Monash University) · Hongdong Li (Australian National University) · Tom Drummond (Monash University) · Zongyuan Ge (Monash University)

分散式加速近端梯度下降
Decentralized Accelerated Proximal Gradient Descent
Haishan Ye (The Chinese University of Hong Kong, Shenzen) · Ziang Zhou (Fudan University) · Luo Luo (The Hong Kong University of Science and Technology) · Tong Zhang (Hong Kong University of Science and Technology)

三重下降和两种过度拟合:它们出现在何处以及为什么出现?
Triple descent and the two kinds of overfitting: where & why do they appear?
Stéphane d’Ascoli (ENS / FAIR) · Levent Sagun () · Giulio Biroli (ENS)

BoxE:知识库完成的盒子嵌入模型
BoxE: A Box Embedding Model for Knowledge Base Completion
Ralph Abboud (University of Oxford) · Ismail Ceylan (University of Oxford) · Thomas Lukasiewicz (University of Oxford) · Tommaso Salvatori (University of Oxford)

图半随机学习的随机神经网络
Graph Stochastic Neural Networks for Semi-supervised Learning
Haibo Wang (Tsinghua University) · Chuan Zhou (Chinese Academy of Sciences) · Xin Chen (Institute for Network Sciences and Cyberspace, Tsinghua University) · Jia Wu (Macquarie University) · Shirui Pan (Monash University) · Jilong Wang (Tsinghua University)

恢复少量目标检测中的负信息
Restoring Negative Information in Few-Shot Object Detection
Yukuan Yang (Tsinghua University) · Fangyun Wei (Microsoft Research Asia) · Miaojing Shi (King’s College London) · Guoqi Li (Tsinghua University)

斯坦因变量梯度下降的非渐近分析
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
Anna Korba (Gatsby Unit - UCL) · Adil SALIM (KAUST) · Michael Arbel (UCL) · Giulia Luise (University College London) · Arthur Gretton (Gatsby Unit, UCL)

稀疏相位检索的连续时间镜像下降方法
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
Fan Wu (University of Oxford) · Patrick Rebeschini (University of Oxford)

增强类学习的无偏风险估计器
An Unbiased Risk Estimator for Learning with Augmented Classes
Yu-Jie Zhang (Nanjing University) · Peng Zhao (Nanjing University) · Lanjihong Ma (Nanjing University) · Zhi-Hua Zhou (Nanjing University)

快速傅立叶卷积
Fast Fourier Convolution
Lu Chi (Peking University) · Borui Jiang (Peking University) · Yadong Mu (Peking University)

自调整演员关键算法
A Self-Tuning Actor-Critic Algorithm
Tom Zahavy (Technion) · Zhongwen Xu (DeepMind) · Vivek Veeriah (University of Michigan) · Matteo Hessel (Google DeepMind) · Junhyuk Oh (DeepMind) · Hado van Hasselt (DeepMind) · David Silver (DeepMind) · Satinder Singh (DeepMind)

真实世界的游戏看起来像陀螺
Real World Games Look Like Spinning Tops
Wojciech Czarnecki (DeepMind) · Gauthier Gidel (Mila) · Brendan Tracey (DeepMind) · Karl Tuyls (DeepMind) · Shayegan Omidshafiei (DeepMind) · David Balduzzi (XTX Markets) · Max Jaderberg (DeepMind)

同意不同意:梯度空间中的自适应集合知识蒸馏
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space
Shangchen Du (SenseTime) · Shan You (SenseTime) · Xiaojie Li (sensetime) · Jianlong Wu (Shandong University) · Fei Wang (SenseTime) · Chen Qian (SenseTime) · Changshui Zhang (Tsinghua University)

学习隐式函数以改变拓扑密度3D形状对应
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
Feng Liu (Michigan State University) · Xiaoming Liu (Michigan State University)

用于分配强化学习的非交叉分位数回归
Non-Crossing Quantile Regression for Distributional Reinforcement Learning
Fan Zhou (Shanghai University of Finance and Economics) · Jianing Wang (Shanghai University of Finance and Economics) · Xingdong Feng (Shanghai University of Finance and Economics)

多代理演员批评的隐式学分分配
Learning Implicit Credit Assignment for Multi-Agent Actor-Critic
Meng Zhou (University of Sydney) · Ziyu Liu (University of Sydney) · Pengwei Sui (University of Sydney) · Yixuan Li (The University of Sydney) · Yuk Ying Chung (The University of Sydney)

何时以及如何解除锁定?使用区间高斯过程的全球COVID-19情景分析和政策评估
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
Zhaozhi Qian (University of Cambridge) · Ahmed Alaa (UCLA) · Mihaela van der Schaar (University of Cambridge)

用图神经网络区分图有多难?
How hard is to distinguish graphs with graph neural networks?
Andreas Loukas (EPFL)

卷积指数流和广义Sylvester流
The Convolution Exponential and Generalized Sylvester Flows
Emiel Hoogeboom (University of Amsterdam) · Victor Garcia Satorras (University of Amsterdam) · Jakub Tomczak (Qualcomm AI Research) · Max Welling (University of Amsterdam / Qualcomm AI Research)

跨域解缠的变分交互信息最大化
Variational Interaction Information Maximization for Cross-domain Disentanglement
HyeongJoo Hwang (Korea Advanced Institute of Science and Technology) · Geon-Hyeong Kim (KAIST) · Seunghoon Hong (KAIST) · Kee-Eung Kim (KAIST)

迈向因果DAG的可扩展贝叶斯学习
Towards Scalable Bayesian Learning of Causal DAGs
Jussi Viinikka (University of Helsinki) · Antti Hyttinen (University of Helsinki) · Johan Pensar (University of Oslo) · Mikko Koivisto (University of Helsinki)

综合数据生成器-顺序和专用
Synthetic Data Generators – Sequential and Private
Olivier Bousquet (Google Brain (Zurich)) · Roi Livni (Tel Aviv University) · Shay Moran (Google AI Princeton)

关于采样器的测试
On Testing of Samplers
Kuldeep S Meel (National University of Singapore) · Yash Pralhad Pote (National University of Singapore) · Sourav Chakraborty (Indian Statistical Institute, India)

NanoFlow:具有亚线性参数复杂度的可扩展标准化流
NanoFlow: scalable normalizing flows with sublinear parameter complexity
Sang-gil Lee (Seoul National University) · Sungwon Kim (Seoul National University) · Sungroh Yoon (Seoul National University)

非策略参与者批评方法的在线元批评学习
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods
Wei Zhou (National University of Defense Technology) · Yiying Li (National University of Defense Technology) · Yongxin Yang (University of Edinburgh ) · Huaimin Wang (National University of Defense Technology) · Timothy Hospedales (University of Edinburgh)

动态亚模最大化
Dynamic Submodular Maximization
Morteza Monemizadeh (Technical University of Eindhoven)

深度学习中不确定性校准的固定激活
Stationary Activations for Uncertainty Calibration in Deep Learning
Lassi Meronen (Aalto University) · Christabella Irwanto (Aalto University) · Arno Solin (Aalto University)

自由还是深度:深贝叶斯神经网络不需要复杂的重量后验近似
Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations
Sebastian Farquhar (University of Oxford) · Lewis Smith (University of Oxford) · Yarin Gal (University of Oxford)

VarGrad:一种用于变量推断的低方差梯度估计器
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
Lorenz Richter (Freie Universität Berlin, BTU Cottbus-Senftenberg, dida) · Ayman Boustati (University of Warwick) · Nikolas Nüsken (Universität Potsdam) · Francisco Ruiz (DeepMind) · Omer Deniz Akyildiz (University of Warwick)

通过强化学习进行基于在线决策的视觉跟踪
Online Decision Based Visual Tracking via Reinforcement Learning
ke Song (Shandong university) · Wei Zhang (Shandong University) · Ran Song (School of Control Science and Engineering, Shandong University) · Yibin Li (Shandong University)

对抗性软优势拟合:无策略优化的模仿学习
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Paul Barde (Quebec AI institute - Mila, McGill) · Julien Roy (Mila) · Wonseok Jeon (MILA, McGill University) · Joelle Pineau (McGill University) · Chris Pal (MILA, Polytechnique Montréal, Element AI) · Derek Nowrouzezahrai (McGill University)

带有动态Bethe-Hessian的稀疏时间演化图中的社区检测
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian
Lorenzo Dall’Amico (GIPSA lab) · Romain Couillet (CentralSupélec) · Nicolas Tremblay (CNRS)

发现强化学习算法
Discovering Reinforcement Learning Algorithms
Junhyuk Oh (DeepMind) · Matteo Hessel (Google DeepMind) · Wojciech Czarnecki (DeepMind) · Zhongwen Xu (DeepMind) · Hado van Hasselt (DeepMind) · Satinder Singh (DeepMind) · David Silver (DeepMind)

大型游戏中的小Nash平衡证书
Small Nash Equilibrium Certificates in Very Large Games
Brian H Zhang (Carnegie Mellon University) · Tuomas Sandholm (CMU, Strategic Machine, Strategy Robot, Optimized Markets)

对复杂环境的影响力增强的在线计划
Influence Augmented Online Planning for Complex Environments
Jinke He (Delft University of Technology) · Miguel Suau (Delft University of Technology) · Frans Oliehoek (TU Delft)

通过分层划分和数据相关分组进行多标签分类
Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping
Shashanka Ubaru (IBM T. J. Watson Research Center) · Sanjeeb Dash (IBM Research) · Arya Mazumdar (University of Massachusetts Amherst) · Oktay Gunluk (Cornell University)

Pontryagin可微编程:一个端到端的学习和控制框架
Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework
Wanxin Jin (Purdue University) · Zhaoran Wang (Northwestern University) · Zhuoran Yang (Princeton) · Shaoshuai Mou (Purdue University)

局部自适应非参数在线学习
Locally-Adaptive Nonparametric Online Learning
Ilja Kuzborskij (DeepMind) · Nicolò Cesa-Bianchi (Università degli Studi di Milano)

最优运输
CO-Optimal Transport
Vayer Titouan (IRISA) · Ievgen Redko (Hubert Curien laboratory) · Rémi Flamary (Université Côte d’Azur) · Nicolas Courty (IRISA, Universite Bretagne-Sud)

通过按估计的预期效用排序进行排名
On ranking via sorting by estimated expected utility
Clement Calauzenes (Criteo) · Nicolas Usunier (Facebook AI Research)

GANSpace:发现可解释的GAN控件
GANSpace: Discovering Interpretable GAN Controls
Erik Härkönen (Aalto University) · Aaron Hertzmann (Adobe) · Jaakko Lehtinen (Aalto University & NVIDIA) · Sylvain Paris (Adobe)

通过软统一学习不变式
Learning Invariants through Soft Unification
Nuri Cingillioglu (Imperial College London) · Alessandra Russo (Imperial College London)

多阶段影响功能
MULTI-STAGE INFLUENCE FUNCTION
Hongge Chen (MIT) · Si Si (Google Research) · Yang Li (Google) · Ciprian Chelba (Google) · Sanjiv Kumar (Google Research) · Duane Boning (Massachusetts Institute of Technology) · Cho-Jui Hsieh (UCLA)

不完全因果知识下的算法求助:一种概率方法
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi (UWaterloo) · Julius von Kügelgen (MPI for Intelligent Systems, Tübingen & University of Cambridge) · Bernhard Schölkopf (MPI for Intelligent Systems) · Isabel Valera (Max Planck Institute for Intelligent Systems)

无监督模型自适应的基于模型的策略优化
Model-based Policy Optimization with Unsupervised Model Adaptation
Jian Shen (Shanghai Jiao Tong University) · Han Zhao (Carnegie Mellon University) · Weinan Zhang (Shanghai Jiao Tong University) · Yong Yu (Shanghai Jiao Tong Unviersity)

Covid-19在德国传播的原因分析
Causal analysis of Covid-19 Spread in Germany
Atalanti Mastakouri (Max Planck Institute for Intelligent Systems) · Bernhard Schölkopf (MPI for Intelligent Systems)

在Stackelberg游戏中最佳地欺骗学习领导者
Optimally Deceiving a Learning Leader in Stackelberg Games
Georgios Birmpas (University of Oxford) · Jiarui Gan (University of Oxford) · Alexandros Hollender (University of Oxford) · Francisco Marmolejo (University of Oxford) · Ninad Rajgopal (University of Oxford) · Alexandros Voudouris (University of Essex)

正则化黑匣子模型以提高可解释性
Regularizing Black-box Models for Improved Interpretability
Gregory Plumb (Carnegie Mellon University) · Maruan Al-Shedivat (Carnegie Mellon University) · Ángel Alexander Cabrera (Carnegie Mellon University) · Adam Perer (Carnegie Mellon University) · Eric Xing (Petuum Inc. / Carnegie Mellon University) · Ameet Talwalkar (CMU)

不平衡数据集的后验重新校准
Posterior Re-calibration for Imbalanced Datasets
Junjiao Tian (Georgia Institute of Technology) · Yen-Cheng Liu (Georgia Tech) · Nathaniel Glaser (Georgia Institute of Technology) · Yen-Chang Hsu (Georgia Institute of Technology) · Zsolt Kira (Georgia Institute of Techology)

在分类抽样中中和自选偏差
Neutralizing Self-Selection Bias in Sampling for Sortition
Bailey Flanigan (Carnegie Mellon University) · Paul Goelz (Carnegie Mellon University) · Anupam Gupta (Carnegie Mellon University) · Ariel Procaccia (Harvard University)

多主体强化学习的共享经验演员批评
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Filippos Christianos (University of Edinburgh) · Lukas Schäfer (University of Edinburgh) · Stefano Albrecht (University of Edinburgh)

学习生成对抗网络的语义感知标准化
Learning Semantic-aware Normalization for Generative Adversarial Networks
Heliang Zheng (University of Science and Technology of China) · Jianlong Fu (Microsoft Research) · Yanhong Zeng (Sun Yat-sen University) · Zheng-Jun Zha (University of Science and Technology of China) · Jiebo Luo (U. Rochester)

综合任务,用于基于块的编程
Synthesizing Tasks for Block-based Programming
Umair Ahmed (National University of Singapore) · Maria Christakis (MPI-SWS) · Aleksandr Efremov (MPI-SWS) · Nigel Fernandez (MPI-SWS) · Ahana Ghosh (MPI-SWS) · Abhik Roychoudhury (National University of Singapore) · Adish Singla (MPI-SWS)

GOCor:将全球优化的通讯量引入您的神经网络
GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
Prune Truong (ETH Zurich) · Martin Danelljan (ETH Zurich) · Luc V Gool (Computer Vision Lab, ETH Zurich) · Radu Timofte (ETH Zurich)

高维神经记录的潜在动态因素分析
Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings
Heejong Bong (Carnegie Mellon University) · Zongge Liu (Carnegie Mellon University) · Zhao Ren (University of Pittsburgh) · Matthew Smith (Carnegie Mellon University) · Valerie Ventura (Carnegie Mellon University) · Kass E Robert (CMU)

平衡的Meta-Softmax用于长尾视觉识别
Balanced Meta-Softmax for Long-Tailed Visual Recognition
Ren Jiawei (Sensetime) · Cunjun Yu (NUS) · shunan sheng (SenseTime International Pte. Ltd.) · Xiao Ma (National University of Singapore) · Haiyu Zhao (SenseTime International Pte Ltd) · Shuai Yi (SenseTime Group Limited) · hongsheng Li (cuhk)

条件变分自动编码器中多峰潜在空间的证据稀疏性
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders
Masha Itkina (Stanford University) · Boris Ivanovic (Stanford University) · Ransalu Senanayake (Stanford University) · Mykel J Kochenderfer (Stanford University) · Marco Pavone (Stanford University)

通过动态前馈网络的生物信用分配
Biological credit assignment through dynamic inversion of feedforward networks
William Podlaski (Champalimaud Research) · Christian K. Machens (Champalimaud Research)

一种从阳性和未标记数据中学习的变体方法
A Variational Approach for Learning from Positive and Unlabeled Data
Hui Chen (Tongji University) · Fangqing Liu (Tongji University) · Yin Wang (Tongji University) · Liyue Zhao (Cloudwalk Inc.) · Hao Wu (Tongji University)

高维相位检索:统计和计算相变
Phase retrieval in high dimensions: Statistical and computational phase transitions
Antoine Maillard (Ecole Normale Supérieure) · Bruno Loureiro (IPhT Saclay) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL) · Lenka Zdeborová (University Paris-Saclay & EPFL)

神经网络学习和记忆(几乎)没有过度参数化
Neural Networks Learning and Memorization with (almost) no Over-Parameterization
Amit Daniely (Hebrew University and Google Research)

从有限到可数武装土匪
From Finite to Countable-Armed Bandits
Anand Kalvit (Columbia Business School) · Assaf Zeevi (Columbia University)

用几何詹森-香农散度约束变分推理
Constraining Variational Inference with Geometric Jensen-Shannon Divergence
Jacob Deasy (University of Cambridge) · Nikola Simidjievski (University of Cambridge) · Pietro Lió (University of Cambridge)

通过距离感知进行确定性深度学习的简单原则性不确定性估计
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Liu (Google Research / Harvard) · Zi Lin (Google) · Shreyas Padhy (Google) · Dustin Tran (Google Brain) · Tania Bedrax Weiss (Google) · Balaji Lakshminarayanan (Google Brain)

对抗性学习以实现强大的深度聚类
Adversarial Learning for Robust Deep Clustering
Xu Yang (Xidian University) · Cheng Deng (Xidian University) · Kun Wei (Xidian University) · Junchi Yan (Shanghai Jiao Tong University) · Wei Liu (Tencent AI Lab)

大多数ReLU网络都遭受
Most ReLU Networks Suffer from

2
对抗性扰动
Adversarial Perturbations
Amit Daniely (Hebrew University and Google Research) · Hadas Shacham (Hebrew University)

高效非参数强盗探索的子采样
Sub-sampling for Efficient Non-Parametric Bandit Exploration
Dorian Baudry (CNRS/INRIA) · Emilie Kaufmann (CNRS) · Odalric-Ambrym Maillard (INRIA)

神经元合并:补偿修剪的神经元
Neuron Merging: Compensating for Pruned Neurons
Woojeong Kim (Korea Institute of Science and Technology) · Suhyun Kim (Korea Institute of Science and Technology) · Mincheol Park (Korea Institute of Science and Technology) · Geunseok Jeon (Korea Institute of Science and Technology)

代理导航的语言和视觉实体关系图
Language and Visual Entity Relationship Graph for Agent Navigation
Yicong Hong (Australian National University) · Cristian Rodriguez (Australian National University) · Yuankai Qi (University of Adelaide ) · Qi Wu (University of Adelaide) · Stephen Gould (ANU)

可交换神经ODE用于集合建模
Exchangeable Neural ODE for Set Modeling
Yang Li (UNC-Chapel Hill) · Haidong Yi (Department of Computer Science, UNC Chapel-Hill) · Christopher Bender (The University of North Carolina) · Siyuan Shan (UNC Chapel Hill) · Junier Oliva (UNC - Chapel Hill)

从观测数据中学习鲁棒的决策策略
Learning Robust Decision Policies from Observational Data
Muhammad Osama (Uppsala University) · Dave Zachariah (Uppsala University) · Peter Stoica (Uppsala University)

零资源知识基础的对话产生
Zero-Resource Knowledge-Grounded Dialogue Generation
Linxiao Li (Peking University) · Can Xu (microsoft) · Wei Wu (Meituan-Dianping Group) · YUFAN ZHAO (Microsoft) · Xueliang Zhao (Peking University) · Chongyang Tao (Microsoft)

迁移学习的组合视角
A Combinatorial Perspective on Transfer Learning
Jianan Wang (DeepMind) · Eren Sezener (DeepMind) · David Budden (DeepMind) · Marcus Hutter (DeepMind) · Joel Veness (Deepmind)

在差异化隐私中平滑限制用户贡献
Smoothly Bounding User Contributions in Differential Privacy
Alessandro Epasto (Google) · Mohammad Mahdian (Google Research) · Jieming Mao (Google Research) · Vahab Mirrokni (Google Research NYC) · Lijie Ren (Google)

线性系统的鲁棒自适应控制:超越二次成本
Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs
Edouard Leurent (INRIA) · Odalric-Ambrym Maillard (INRIA) · Denis Efimov (Inria)

从响应混合中恢复稀疏线性分类器
Recovery of sparse linear classifiers from mixture of responses
Venkata Gandikota (University of Massachusetts, Amherst) · Arya Mazumdar (University of Massachusetts Amherst) · Soumyabrata Pal (University of Massachusetts Amherst)

图随机神经网络用于图的半监督学习
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
Wenzheng Feng (Tsinghua University) · Jie Zhang (Webank Co.,Ltd) · Yuxiao Dong (Microsoft) · Yu Han (Tsinghua University) · Huanbo Luan (Tsinghua University) · Qian Xu (WeBank) · Qiang Yang (WeBank and HKUST) · Evgeny Kharlamov (Bosch Center for Artificial Intelligence) · Jie Tang (Tsinghua University)

通过图连接拉普拉斯算子的迭代加权实现鲁棒的多对象匹配
Robust Multi-Object Matching via Iterative Reweighting of the Graph Connection Laplacian
Yunpeng Shi (Princeton University) · Shaohan Li (University of Minnesota) · Gilad Lerman (University of Minnesota)

通过置信度裕度最大化的半监督部分标签学习
Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization
Wei Wang (Southeast University) · Min-Ling Zhang (Southeast University)

更清晰:多尺度神经体系结构搜索以恢复图像
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration
Yuanbiao Gou (College of Computer Science, Sichuan University) · Boyun Li (College of Computer Science, Sichuan University) · Zitao Liu (TAL AI Lab) · Songfan Yang (TAL AI Lab) · Xi Peng (Institute for Infocomm, Research Agency for Science, Technology and Research (A*STAR) Singapore)

学习数据丢失的视频的纠缠表示
Learning Disentangled Representations of Videos with Missing Data
Armand Comas (Northeastern University) · Chi Zhang (Northeastern University) · Zlatan Feric (Northeastern University) · Octavia Camps (Northeastern University) · Rose Yu (University of California, San Diego)

受过元训练的代理实施贝叶斯最优代理
Meta-trained agents implement Bayes-optimal agents
Vladimir Mikulik (Google DeepMind) · Grégoire Delétang (DeepMind) · Tom McGrath (Deepmind) · Tim Genewein (DeepMind) · Miljan Martic (DeepMind) · Shane Legg (DeepMind) · Pedro Ortega (DeepMind)

萤火虫神经体系结构的下降:增长神经网络的通用方法。
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks
Lemeng Wu (UT Austin) · Bo Liu (University of Texas at Austin) · Qiang Liu (UT Austin) · Peter Stone (The University of Texas at Austin)

LoCA后悔:在强化学习中评估基于模型的行为的一致度量
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
Harm Van Seijen (Microsoft Research) · Hadi Nekoei (MILA) · Evan Racah (Mila, Université de Montréal) · Sarath Chandar (Mila / École Polytechnique de Montréal)

借助BONAS缩小基于样本的和一次性神经架构搜索之间的差距
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS
Han Shi (Hong Kong University of Science and Technology) · Renjie Pi (Huawei Noah’s Ark Lab) · Hang Xu (Huawei Noah’s Ark Lab) · Zhenguo Li (Noah’s Ark Lab, Huawei Tech Investment Co Ltd) · James Kwok (Hong Kong University of Science and Technology) · Tong Zhang (Hong Kong University of Science and Technology)

有向图初始卷积网络
Digraph Inception Convolutional Networks
Zekun Tong (National University of Singapore) · Yuxuan Liang (National University of Singapore) · Changsheng Sun (National University of Singapore) · Xinke Li (National University of Singapore) · David Rosenblum (National University of Singapore) · Andrew Lim (National University of Singapore)

监督稀疏编码的对抗鲁棒性
Adversarial Robustness of Supervised Sparse Coding
Jeremias Sulam (Johns Hopkins University) · Ramchandran Muthukumar (Johns Hopkins University) · Raman Arora (Johns Hopkins University)

用神经网络学习离散图形模型
Learning of Discrete Graphical Models with Neural Networks
Abhijith Jayakumar (Indian Institute of Science) · Andrey Lokhov (LANL) · Sidhant Misra (Los Alamos National Laboratory) · Marc Vuffray (Los Alamos National Laboratory)

内核化的信息瓶颈导致在深层网络中生物学上可行的三因素赫比学习
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks
Roman Pogodin (University College London) · Peter E Latham (Gatsby Unit, UCL)

通过学习的匹配成本进行准确的光流估计
Accurate Optical Flow Estimation by Learned Matching Cost
Jianyuan Wang (Australian National University) · Yiran Zhong (Australian National University) · Yuchao Dai (Northwestern Polytechnical University) · Kaihao Zhang (Australian National University) · Pan Ji (NEC Labs) · Hongdong Li (Australian National University)

GRAF:3D感知图像合成的生成辐射场
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
Katja Schwarz (MPI Tuebingen) · Yiyi Liao (MPI Tuebingen) · Michael Niemeyer (Max Planck for Intelligent Systems) · Andreas Geiger (MPI-IS and University of Tuebingen)

通过加权再训练在深度生成模型的潜在空间中进行样本有效的优化
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
Austin Tripp (University of Cambridge) · Erik Daxberger (University of Cambridge) · José Miguel Hernández-Lobato (University of Cambridge)

通过局部子图进行图元学习
Graph Meta Learning via Local Subgraphs
Kexin Huang (Harvard University) · Marinka Zitnik (Harvard University)

CSI:通过分布转移实例的对比学习进行新颖性检测
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
Jihoon Tack (KAIST) · Sangwoo Mo (KAIST) · Jongheon Jeong (KAIST) · Jinwoo Shin (KAIST)

通过有限差分匹配有效学习生成模型
Efficient Learning of Generative Models via Finite-Difference Score Matching
Tianyu Pang (Tsinghua University) · Taufik Xu (Tsinghua University) · Chongxuan LI (Tsinghua University) · Yang Song (Stanford University) · Stefano Ermon (Stanford) · Jun Zhu (Tsinghua University)

基于偏向期望的非凸随机梯度下降的稳健性分析
Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations
Kevin Scaman (Noah’s Ark Lab, Huawei Technologies) · Cedric Malherbe (Huawei Noah’s Ark Lab)

加权多数票的二阶PAC-贝叶斯界
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote
Andres Masegosa (University of Almeria) · Stephan Lorenzen (University of Copenhagen) · Christian Igel (University of Copenhagen) · Yevgeny Seldin (University of Copenhagen)

对抗式分布训练以实现强大的深度学习
Adversarial Distributional Training for Robust Deep Learning
Yinpeng Dong (Tsinghua University) · Zhijie Deng (Tsinghua University) · Tianyu Pang (Tsinghua University) · Hang Su (Tsinghua Univiersity) · Jun Zhu (Tsinghua University)

条件风险值的PAC-贝叶斯界
PAC-Bayesian Bound for the Conditional Value at Risk
Zakaria Mhammedi (The Australian National University and Data61) · Benjamin Guedj (Inria & University College London) · Robert Williamson (ANU)

鲁棒序列次模最大化
Robust Sequence Submodular Maximization
Gamal A Sallam (Temple University) · Zizhan Zheng (Tulane University) · Jie Wu (Temple University) · Bo Ji (Virginia Tech)

用于学习基于能量的潜在变量模型的双层评分匹配
Bi-level Score Matching for Learning Energy-based Latent Variable Models
Fan Bao (Tsinghua University) · Chongxuan LI (Tsinghua University) · Taufik Xu (Tsinghua University) · Hang Su (Tsinghua Univiersity) · Jun Zhu (Tsinghua University) · Bo Zhang (Tsinghua University)

自举神经过程
Bootstrapping neural processes
Juho Lee (KAIST, AITRICS) · Yoonho Lee (AITRICS) · Jungtaek Kim (POSTECH) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics) · Sung Ju Hwang (KAIST, AITRICS) · Yee Whye Teh (University of Oxford, DeepMind)

皮质微电路局部监督的规范模型
A Normative Model of Local Supervision in Cortical Microcircuits
Siavash Golkar (Flatiron Institute) · David Lipshutz (Flatiron Institute) · Yanis Bahroun (Flatiron institute) · Anirvan Sengupta (Rutgers University) · Dmitri Chklovskii (Flatiron Institute, Simons Foundation)

WOR和p:无需替换的l_p采样草图
WOR and p’s: Sketches for l_p-Sampling Without Replacement
Edith Cohen (Google) · Rasmus Pagh (IT University of Copenhagen) · David Woodruff (Carnegie Mellon University)

通过跟踪梯度下降来估计训练数据的影响
Estimating Training Data Influence by Tracking Gradient Descent
Garima Pruthi (Google) · Frederick Liu (Google Inc.) · Satyen Kale (Google) · Mukund Sundararajan (Google LLC)

关于1 / n神经表示和鲁棒性
On 1/n neural representation and robustness
Josue Nassar (Stony Brook University) · Piotr Sokol (Stony Brook University) · Sueyeon Chung (Columbia University) · Kenneth D Harris (UCL) · Il Memming Park (Stony Brook University)

通过超球面嵌入增强对抗训练
Boosting Adversarial Training with Hypersphere Embedding
Tianyu Pang (Tsinghua University) · Xiao Yang (Tsinghua University) · Yinpeng Dong (Tsinghua University) · Taufik Xu (Tsinghua University) · Hang Su (Tsinghua Univiersity) · Jun Zhu (Tsinghua University)

知识密集型NLP任务的检索增强生成
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Patrick Lewis (Facebook AI Research) · Ethan Perez (New York University) · Aleksandra Piktus (Facebook AI) · Fabio Petroni (Facebook AI Research) · Vladimir Karpukhin (Facebook AI Research) · Naman Goyal (Facebook Inc) · Heinrich Küttler (Facebook AI Research) · Mike Lewis (Facebook AI Research) · Wen-tau Yih (Facebook AI Research) · Tim Rocktäschel (Facebook AI Research) · Sebastian Riedel () · Douwe Kiela (Facebook AI Research)

通过稀疏有效地离散化和结构化潜在变量的边际化
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity
Gonçalo Correia (Instituto de Telecomunicações) · Vlad Niculae (Instituto de Telecomunicações) · Wilker Aziz (University of Amsterdam) · André Martins ()

机器学习中与任务无关的样本设计的统计力学框架
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning
Bhavya Kailkhura (Lawrence Livermore National Lab) · Jayaraman J. Thiagarajan (Lawrence Livermore National Labs) · Qunwei Li (Ant Financial) · Jize Zhang (Lawrence Livermore National Laboratory) · Yi Zhou (University of Utah) · Timo Bremer (Lawrence Livermore National Laboratory)

OrganITE:采用个体治疗效果的最佳器官移植
OrganITE: Optimal organ transplants using an individual treatment effect
Jeroen Berrevoets (University of Cambridge) · James Jordon (University of Oxford) · Ioana Bica (University of Oxford) · alexander gimson (Cambridge University Hospitals) · Mihaela van der Schaar (University of Cambridge)

铰链损耗分类的浅层网络动力学解析理论
An analytic theory of shallow networks dynamics for hinge loss classification
Franco Pellegrini (École normale supérieure, Paris) · Giulio Biroli (ENS)

解析深度神经网络的一致特征选择
Consistent feature selection for analytic deep neural networks
Vu Dinh (University of Delaware) · Lam Ho (University of Dalhousie)

用遗传探索指导深度分子优化
Guiding Deep Molecular Optimization with Genetic Exploration
Sung-Soo Ahn (KAIST) · Junsu Kim (KAIST) · Hankook Lee (Korea Advanced Institute of Science and Technology) · Jinwoo Shin (KAIST)

带有批次评估的多样性指导的多目标贝叶斯优化
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations
Mina Konakovic Lukovic (Massachusetts Institute of Technology) · Yunsheng Tian (Massachusetts Institute of Technology) · Wojciech Matusik (MIT)

在监督降维中保留类别和邻居的转向失真
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction
Benoît Colange (CEA) · Jaakko Peltonen (University of Tampere) · Michael Aupetit (Qatar Computing Research Institute) · Denys Dutykh (CNRS) · Sylvain Lespinats (CEA Tech, INES, Annecy, France)

去对比性学习
Debiased Contrastive Learning
Ching-Yao Chuang (MIT) · Joshua Robinson (MIT) · Yen-Chen Lin (MIT) · Antonio Torralba (MIT) · Stefanie Jegelka (MIT)

无监督深度学习中Jacobian项的相对梯度优化
Relative gradient optimization of the Jacobian term in unsupervised deep learning
Luigi Gresele (MPI for Intelligent Systems, Tübingen) · Giancarlo Fissore (Inria) · Adrián Javaloy (Saarland University) · Bernhard Schölkopf (MPI for Intelligent Systems) · Aapo Hyvarinen (University of Helsinki)

过度拟合对于基本追求可能无害,但只能达到一定程度
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree
Peizhong Ju (Purdue University) · Xiaojun Lin (Purdue University) · Jia Liu (The Ohio State University)

循环随机网络作为优化的内核机器
Recurrent Random Networks as Optimized Kernel Machines
Sandra Nestler (Juelich Research Centre) · Christian Keup (Juelich Research Centre) · David Dahmen (Jülich Research Centre) · Matthieu Gilson (Juelich Forschungszentrum) · Holger Rauhut (RWTH Aachen University) · Moritz Helias (Juelich Research Centre)

紧凑的任务表示作为高阶大脑活动的规范模型
Compact task representation as a normative model for higher-order brain activity
Severin Berger (Champalimaud Centre for the Unknown) · Christian K Machens (Champalimaud Centre for the Unknown)

在线学习中的无度量个体公平
Metric-Free Individual Fairness in Online Learning
Yahav Bechavod (Hebrew University of Jerusalem) · Christopher Jung (University of Pennsylvania) · Steven Wu (Carnegie Mellon University)

情境强盗的经验可能性
Empirical Likelihood for Contextual Bandits
Paul Mineiro (Microsoft) · Nikos Karampatziakis (Microsoft) · John Langford (Microsoft Research New York)

有约束的深度逆Q学习
Deep Inverse Q-learning with Constraints
Gabriel Kalweit (University of Freiburg) · Maria Huegle (University of Freiburg) · Moritz Werling (BMWGroup, Unterschleissheim) · Joschka Boedecker (University of Freiburg)

具有线性函数逼近的分散TD跟踪及其有限时间分析
Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis
Gang Wang (Beijing Institute of Technology) · Songtao Lu (IBM Research) · Georgios Giannakis (University of Minnesota) · Gerald Tesauro (IBM TJ Watson Research Center) · Jian Sun (Beijing Insitute of Technology)

不确定性量化的异质治疗效果的鲁棒递归划分
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Hyun-Suk Lee (Sejong University) · Yao Zhang (University of Cambridge) · William Zame (UCLA) · Cong Shen (University of Virginia) · Jang-Won Lee (Yonsei University) · Mihaela van der Schaar (University of Cambridge)

约束子模优化的全动态算法
Fully Dynamic Algorithm for Constrained Submodular Optimization
Silvio Lattanzi (Google Research) · Slobodan Mitrović (MIT) · Ashkan Norouzi-Fard (Google Research) · Jakub Tarnawski (Microsoft Research) · Morteza Zadimoghaddam (Google Research)

认证策略证明性拍卖网络
Certifying Strategyproof Auction Networks
Michael Curry (University of Maryland) · Ping-yeh Chiang (University of Maryland, College Park) · Tom Goldstein (University of Maryland) · John Dickerson (University of Maryland)

注意力集中的快速变压器
Fast Transformers with Clustered Attention
Apoorv Vyas (Idiap Research Institute) · Angelos Katharopoulos (Idiap) · François Fleuret (University of Geneva)

通过图上的学习变换同步实现人体之间的密集对应
Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs
Xiangru Huang (University of Texas at Austin) · Haitao Yang (University of Texas at Austin) · Etienne Vouga (The University of Texas at Austin) · Qixing Huang (The University of Texas at Austin)

混合和匹配:从混合物分布中学习模型的乐观树搜索方法
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
Matthew Faw (University of Texas at Austin) · Rajat Sen (Amazon) · Karthikeyan Shanmugam (IBM Research, NY) · Constantine Caramanis (UT Austin) · Sanjay Shakkottai (University of Texas at Austin)

充分利用平均值:强化学习中的KL正则化分析
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning
Nino Vieillard (Google Brain) · Tadashi Kozuno (Okinawa Institute of Science and Technology) · Bruno Scherrer (INRIA) · Olivier Pietquin (Google Research Brain Team) · Remi Munos (DeepMind) · Matthieu Geist (Google Brain)

AdaShare:学习共享内容以进行有效的深度多任务学习
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Ximeng Sun (Boston University) · Rameswar Panda (MIT-IBM Watson AI Lab) · Rogerio Feris (MIT-IBM Watson AI Lab, IBM Research) · Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)

通过消息传递构建强大的等变图神经网络
Building powerful and equivariant graph neural networks with message-passing
Clément Vignac (EPFL) · Andreas Loukas (EPFL) · Pascal Frossard (EPFL)

强化学习中与任务无关的探索
Task-agnostic Exploration in Reinforcement Learning
Xuezhou Zhang (UW-Madison) · Yuzhe Ma (University of Wisconsin-Madison) · Adish Singla (MPI-SWS)

基于能量模型的合成视觉生成
Compositional Visual Generation with Energy Based Models
Yilun Du (MIT) · Shuang Li (MIT) · Igor Mordatch (Google)

分布鲁棒参数最大似然估计
Distributionally Robust Parametric Maximum Likelihood Estimation
Viet Anh Nguyen (Stanford University) · Xuhui Zhang (Stanford University) · Jose Blanchet (Stanford University) · Angelos Georghiou (University of Cyprus)

圆锥下降及其在正半定矩阵上的记忆效率优化中
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices
John Duchi (Stanford) · Oliver Hinder (University of Pittsburgh) · Andrew Naber (Stanford University) · Yinyu Ye (Standord)

并行语音合成的频谱能量距离
A Spectral Energy Distance for Parallel Speech Synthesis
Alexey Gritsenko (Google) · Tim Salimans (Google Brain Amsterdam) · Rianne van den Berg (Google Brain) · Jasper Snoek (Google Brain) · Nal Kalchbrenner (Google Brain)

编程语言的无监督翻译
Unsupervised Translation of Programming Languages
Baptiste Roziere (Facebook AI Research) · Marie-Anne Lachaux (Facebook AI Research) · Lowik Chanussot (Facebook AI Research) · Guillaume Lample (Facebook AI Research)

STEER:神经ODE的简单时间正则化
STEER : Simple Temporal Regularization For Neural ODE
Arnab Ghosh (University of Oxford) · Harkirat Behl (University of Oxford) · Emilien Dupont (Oxford University) · Philip Torr (University of Oxford) · Vinay Namboodiri (University of Bath)

关系事件的连续时间网络中的可伸缩性和一致估计
Scalable and Consistent Estimation in Continuous-time Networks of Relational Events
Makan Arastuie (University of Toledo) · Subhadeep Paul (The Ohio State University) · Kevin Xu (University of Toledo)

颂歌颂
An Ode to an ODE
Krzysztof Choromanski (Google Brain Robotics & Columbia University) · Jared Quincy Davis (Google Brain) · Valerii Likhosherstov (University of Cambridge) · Xingyou Song (Google Brain) · Vikas Sindhwani (Google) · Jean-Jacques Slotine (Massachusetts Institute of Technology) · Jacob Varley (Google) · Honglak Lee (Google Brain) · Adrian Weller (Cambridge, Alan Turing Institute)

在分层强化学习中生成邻接约束子目标
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
Tianren Zhang (Tsinghua University) · Shangqi Guo (Tsinghua University) · Tian Tan (Stanford University) · Xiaolin Hu (Tsinghua University) · Feng Chen (Tsinghua University)

高维中具有低秩数据的近似交叉验证
Approximate Cross-Validation with Low-Rank Data in High Dimensions
William Stephenson (MIT) · Madeleine Udell (Cornell University) · Tamara Broderick (MIT)

类不平衡数据中因式表示的生成建模
Generative Modeling of Factorized Representations in Class-Imbalanced Data
Utkarsh Ojha (University of California, Davis) · Krishna Kumar Singh (University of California Davis) · Cho-Jui Hsieh (UCLA) · Yong Jae Lee (University of California, Davis)

高对比度的“艳丽”​​图像改善了视觉皮层的深度神经网络模型的训练
High-contrast “gaudy” images improve the training of deep neural network models of visual cortex
Benjamin Cowley (Princeton University) · Jonathan W Pillow (Princeton University)

具有长期记忆的在线多任务学习
Online Multitask Learning with Long-Term Memory
Mark Herbster (University College London) · Stephen Pasteris (University College London) · Fai Yu Lisa Tse (University College London)

通过自训练函授的无监督学习对象地标
Unsupervised Learning of Object Landmarks via Self-Training Correspondence
Dimitrios Mallis (Computer Vision Laboratory - University of Nottingham) · Enrique Sanchez (Samsung AI Centre) · Matthew Bell (University of Nottingham) · Georgios Tzimiropoulos (Queen Mary University of London)

深度神经网络中基于完整性感知概念的解释
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
Chih-Kuan Yeh (Carnegie Mellon University) · Been Kim (Google) · Sercan Arik (Google) · Chun-Liang Li (Google) · Tomas Pfister (Google) · Pradeep Ravikumar (Carnegie Mellon University)

使用反馈图进行强化学习
Reinforcement Learning with Feedback Graphs
Christoph Dann (Carnegie Mellon University) · Yishay Mansour (Google) · Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research) · Ayush Sekhari (Cornell University) · Karthik Sridharan (Cornell University)

图形神经网络可以计数子结构吗?
Can Graph Neural Networks Count Substructures?
Zhengdao Chen (New York University) · Lei Chen (New York University) · Soledad Villar (New York University) · Joan Bruna (NYU)

体验重放中损失函数与非均匀抽样之间的等价关系
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
Scott Fujimoto (McGill University) · David Meger (McGill University) · Doina Precup (McGill University / Mila / DeepMind Montreal)

通过多视图观察学习多对象场景的神经场景表示
Learning Neural Scene Representations of Multi-object Scenes With Multi-view Observations
Nanbo Li (University of Edinburgh) · C E (University of Edinburgh) · Robert Fisher (University of Edinburgh)

在线推荐系统中的遗憾
Minimal Regret in Online Recommendation Systems
Kaito Ariu (KTH) · Narae Ryu (KAIST) · Se-Young Yun (KAIST) · Alexandre Proutiere (KTH)

通过深度强化学习进行存储高效且动态灵活的运行时通道修剪
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning
Jianda Chen (Nanyang Technological University) · Shangyu Chen (Nanyang Technological University, Singapore) · Sinno Jialin Pan (Nanyang Technological University, Singapore)

鲁棒核超参数车削的统计成本
The Statistical Cost of Robust Kernel Hyperparameter Turning
Raphael Meyer (New York University) · Christopher Musco (New York University)

协作多层盗贼优化
Cooperative Multi-Player Bandit Optimization
Ilai Bistritz (Stanford) · Nicholas Bambos (Stanford University)

增强神经ODE中的二阶行为
On Second Order Behaviour in Augmented Neural ODEs
Alexander Norcliffe (University College London) · Cristian Bodnar (University of Cambridge) · Ben Day (University of Cambridge) · Nikola Simidjievski (University of Cambridge) · Pietro Lió (University of Cambridge)

选择强盗
Choice Bandits
Arpit Agarwal (University of Pennsylvania) · Nicholas Johnson (University of Pennsylvania) · Shivani Agarwal (University of Pennsylvania)

深层神经网络的近似速率相图
The phase diagram of approximation rates for deep neural networks
Dmitry Yarotsky (Skolkovo Institute of Science and Technology) · Anton Zhevnerchuk (Skolkovo Institute of Science and Technology)

稀疏张量PCA中的全有或全无现象
The All-or-Nothing Phenomenon in Sparse Tensor PCA
Jonathan Niles-Weed (NYU) · Ilias Zadik (NYU)

解决整数线性程序的通用大邻域搜索框架
A General Large Neighborhood Search Framework for Solving Integer Linear Programs
Jialin Song (Caltech) · ravi lanka (rakuten) · Yisong Yue (Caltech) · Bistra Dilkina (University of Southern California)

与无法观察的混杂因素的因果模仿学习
Causal Imitation Learning With Unobserved Confounders
Junzhe Zhang (Columbia University) · Daniel Kumor (Purdue University) · Elias Bareinboim (Columbia University)

旨在改进非固定MDP的安全策略
Towards Safe Policy Improvement for Non-Stationary MDPs
Yash Chandak (University of Massachusetts Amherst) · Scott Jordan (University of Massachusetts Amherst) · Georgios Theocharous (Adobe Research) · Martha White (University of Alberta) · Philip Thomas (University of Massachusetts Amherst)

CSER:具有错误重置的高效通信SGD
CSER: Communication-efficient SGD with Error Reset
Cong Xie (University of Illinois Urbana-Champaign) · Shuai Zheng (Amazon Web Services) · Oluwasanmi Koyejo (UIUC) · Indranil Gupta (UIUC) · Mu Li (Amazon) · Haibin Lin (Amazon Web Services)

图嵌入中的流形结构
Manifold structure in graph embeddings
Patrick Rubin-Delanchy (University of Bristol)

k聚类问题的滑动窗口算法
Sliding Window Algorithms for k-Clustering Problems
Michele Borassi (Google Switzerland GmbH) · Alessandro Epasto (Google) · Silvio Lattanzi (Google Research) · Sergei Vassilvitskii (Google) · Morteza Zadimoghaddam (Google Research)

软模块化的多任务强化学习
Multi-Task Reinforcement Learning with Soft Modularization
Ruihan Yang (UC San Diego) · Huazhe Xu (UC Berkeley) · YI WU (UC Berkeley) · Xiaolong Wang (UCSD/UC Berkeley)

聆听沉默的声音以进行语音降噪
Listening to Sounds of Silence for Speech Denoising
Ruilin Xu (Columbia University) · Rundi Wu (Columbia University) · Yuko Ishiwaka (SoftBank Corp.) · Carl Vondrick (Columbia University) · Changxi Zheng (Columbia University)

视觉对比的软对比学习
Soft Contrastive Learning for Visual Localization
Janine Thoma (ETH Zurich) · Danda Pani Paudel (ETH Zürich) · Luc V Gool (Computer Vision Lab, ETH Zurich)

具有条件分布匹配和广义标签移位的域自适应
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Remi Tachet des Combes (Microsoft Research Montreal) · Han Zhao (Carnegie Mellon University) · Yu-Xiang Wang (UC Santa Barbara) · Geoffrey Gordon (MSR Montréal & CMU)

通过SGD对l1损失进行在线稳健回归
Online Robust Regression via SGD on the l1 loss
Scott Pesme (EPFL) · Nicolas Flammarion (EPFL)

高斯门控线性网络
Gaussian Gated Linear Networks
David Budden (DeepMind) · Adam Marblestone () · Eren Sezener (DeepMind) · Tor Lattimore (DeepMind) · Gregory Wayne (Google DeepMind) · Joel Veness (Deepmind)

深度学习的光谱总变化分解
Deeply Learned Spectral Total Variation Decomposition
Tamara Grossmann (University of Cambridge) · Yury Korolev (University of Cambridge) · Guy Gilboa (Technion) · Carola Schoenlieb (Cambridge University)

结构模型的近似交叉验证
Approximate Cross-Validation for Structured Models
Soumya Ghosh (IBM Research) · William Stephenson (MIT) · Tin D Nguyen (MIT) · Sameer Deshpande (Wharton Statistics) · Tamara Broderick (MIT)

点云上的自我指导的少量学习
Self-Supervised Few-Shot Learning on Point Clouds
Charu Sharma (Indian Institute of Technology Hyderabad) · Manohar Kaul (IITH)

变式无模态对象完成
Variational Amodal Object Completion
Huan Ling (University of Toronto, NVIDIA) · David Acuna (University of Toronto, NVIDIA) · Karsten Kreis (NVIDIA) · Seung Wook Kim (University of Toronto) · Sanja Fidler (University of Toronto)

NetHack学习环境
The NetHack Learning Environment
Heinrich Küttler (Facebook AI Research) · Nantas Nardelli (University of Oxford) · Alexander Miller (Facebook AI Research) · Roberta Raileanu (NYU) · Marco Selvatici (Imperial College London) · Edward Grefenstette (DeepMind) · Tim Rocktäschel (University College London Facebook AI Research)

秘书和在线匹配问题以及机器学习建议
Secretary and Online Matching Problems with Machine Learned Advice
Antonios Antoniadis (University of Cologne) · Themis Gouleakis (Max Planck Institute for Informatics) · Pieter Kleer (Max Planck Institute for Informatics) · Pavel Kolev (Max-Planck-Institut für Informatik)

利用信息瓶颈进行竞争性生成分类训练归一化流
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
Lynton Ardizzone (Heidelberg University) · Radek Mackowiak (Robert Bosch GmbH) · Carsten Rother (University of Heidelberg) · Ullrich Köthe (University of Heidelberg)

用于设备学习的分布式蒸馏
Distributed Distillation for On-Device Learning
Ilai Bistritz (Stanford) · Ariana Mann (Stanford University) · Nicholas Bambos (Stanford University)

通过对抗训练去除MRI束带
MRI Banding Removal via Adversarial Training
Aaron Defazio (Facebook AI Research) · Tullie Murrell (Facebook AI Research) · Michael Recht (New York University School of Medicine)

使用差异化的优化工具进行学习
Learning with Differentiable Pertubed Optimizers
Quentin Berthet (Google Brain) · Mathieu Blondel (Google) · Olivier Teboul (Ecole Centrale Paris) · Marco Cuturi (Google Brain & CREST - ENSAE) · Jean-Philippe Vert () · Francis Bach (INRIA - Ecole Normale Superieure)

具有初级和次级损失的在线学习
Online Learning with Primary and Secondary Losses
Avrim Blum (Toyota Technological Institute at Chicago) · Han Shao (Toyota Technological Institute at Chicago)

视觉幻象:基于统计的计算模型
Visual Illusions: Statistics-based Computational Model
Elad Hirsch (Technion) · Ayellet Tal (Technion)

加权QMIX:针对深度多智能体强化学习的单调值函数分解
Weighted QMIX: Improving Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid (University of Oxford) · Gregory Farquhar (University of Oxford) · Bei Peng (University of Oxford) · Shimon Whiteson (University of Oxford)

在无导数优化和连续匪徒中开发高阶平滑度
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits
Arya Akhavan (ENSAE - IIT) · Massimiliano Pontil (IIT & UCL) · Alexandre Tsybakov (CREST, ENSAE)

决策树作为分区机来表征其概括特性
Decision trees as partitioning machines to characterize their generalization properties
Jean-Samuel Leboeuf (Université Laval) · Frédéric LeBlanc (Université de Moncton) · Mario Marchand (Université Laval)

图神经网络的归因
Attribution for Graph Neural Networks
Benjamin Sanchez-Lengeling (Google Research) · Jennifer Wei (Google Research) · Brian Lee (Google Inc.) · Emily Reif (Google) · Peter Wang (Columbia University) · Wesley Wei Qian (University of Illinois at Urbana-Champaign) · Kevin McCloskey (Google) · Lucy Colwell (Google) · Alexander Wiltschko (Google Brain)

细节在于魔鬼:通过微观模型进行宏观预测的框架
The Devil is in the Detail: a Framework for Macroscopic Prediction via Microscopic Models
Yingxiang Yang (ByteDance) · Negar Kiyavash (École Polytechnique Fédérale de Lausanne) · Le Song (Georgia Institute of Technology) · Niao He (UIUC)

关系推理的更好集合表示
Better Set Representations For Relational Reasoning
Qian Huang (Cornell University) · Horace He (Cornell University) · Abhay Singh (Cornell University) · Yan Zhang (University of Southampton) · Ser Nam Lim (Facebook AI) · Austin Benson (Cornell University)

MDP同态网络:强化学习中的组对称
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
Elise van der Pol (University of Amsterdam) · Daniel Worrall (University of Amsterdam) · Herke van Hoof (University of Amsterdam) · Frans Oliehoek (TU Delft) · Max Welling (University of Amsterdam / Qualcomm AI Research)

提前停止的镜像下降的统计复杂度
The Statistical Complexity of Early-Stopped Mirror Descent
Tomas Vaskevicius (University of Oxford) · Varun Kanade (University of Oxford) · Patrick Rebeschini (University of Oxford)

非单调变分不等式的乐观对偶外推
Optimistic Dual Extrapolation for Non-monotone Variational Inequality
Chaobing Song (Tsinghua University) · Yichao Zhou (UC Berkeley) · Zhengyuan Zhou (Stanford University) · Yong Jiang (Tsinghua) · Yi Ma (UC Berkeley)

分布稳健的局部非参数条件估计
Distributionally Robust Local Non-parametric Conditional Estimation
Viet Anh Nguyen (Stanford University) · Fan Zhang (Stanford University) · Jose Blanchet (Stanford University) · Erick Delage (HEC Montréal) · Yinyu Ye (Standord)

不对称的Shapley值:将因果知识纳入模型不可知的可解释性
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability
Christopher Frye (Faculty) · Colin Rowat (University of Birmingham) · Ilya Feige (Faculty)

通过迭代多重滤波的列表可分解均值估计
List-Decodable Mean Estimation via Iterative Multi-Fitering
Ilias Diakonikolas (UW Madison) · Daniel Kane (UCSD) · Daniel Kongsgaard (UCSD)

使用原则最佳运输方向进行分类的充分降维
Sufficient dimension reduction for classification using principle optimal transport direction
Cheng Meng (Renmin University of China) · Jun Yu (Beijing Institute of Technology) · Jingyi Zhang (Tsinghua University) · Wenxuan Zhong () · Ping Ma (University of Georgia)

关于超网络的模块化
On the Modularity of Hypernetworks
Tomer Galanti (Tel Aviv University) · Lior Wolf (Facebook AI Research)

一致收敛与低范数插值学习
On Uniform Convergence and Low-Norm Interpolation Learning
Lijia Zhou (University of Chicago) · D.J. Sutherland (TTI-Chicago) · Nati Srebro (TTI-Chicago)

非凸SGD学习具有对抗性标签噪声的半空间
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise
Ilias Diakonikolas (UW Madison) · Vasilis Kontonis (University of Wisconsin-Madison) · Christos Tzamos (UW Madison) · Nikos Zarifis (University of Wisconsin-Madison)

通过使用相对熵编码对它们的潜在表示进行编码来压缩图像
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding
Gergely Flamich (University of Cambridge) · Marton Havasi (University of Cambridge) · José Miguel Hernández-Lobato (University of Cambridge)

通过风险估计的生物医学假设产生的时间正向无标记学习
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
Uchenna Akujuobi (King Abdullah University of Science and Technology) · Jun Chen (King Abdullah University of Science and Techonology) · Mohamed Elhoseiny (KAUST and Stanford University) · Michael Spranger (Sony) · Xiangliang Zhang (“ King Abdullah University of Science and Technology, Saudi Arabia”)

学习基于潜在空间能量的先验模型
Learning Latent Space Energy-Based Prior Model
Bo Pang (University of California Los Angeles) · Tian Han (Stevens Institute of Technology) · Erik Nijkamp (UCLA) · Song-Chun Zhu (UCLA) · Ying Nian Wu (University of California, Los Angeles)

CoinDICE:非政策置信区间估计
CoinDICE: Off-Policy Confidence Interval Estimation
Bo Dai (Google Brain) · Ofir Nachum (Google Brain) · Yinlam Chow (Google Research) · Lihong Li (Google Research) · Csaba Szepesvari (DeepMind / University of Alberta) · Dale Schuurmans (Google Brain & University of Alberta)

通过基于梯度的算法在平滑的在线凸优化中利用预测
Leveraging predictions in smoothed online convex optimization via gradient-based algorithms
Yingying Li (Harvard University) · Na Li (Harvard University)

UCSG-NET-建设性实体几何树的无监督发现
UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree
Kacper Kania (Wrocław University of Science and Technology) · Maciej Zieba (Wroclaw University of Science and Technology, Tooploox) · Tomasz Kajdanowicz (Wroclaw University of Science and Technology)

大随机图上图卷积网络的收敛性和稳定性
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
Nicolas Keriven (CNRS, GIPSA-lab) · Alberto Bietti (Inria) · Samuel Vaiter (CNRS)

标签高效深度学习的多方面不确定性估计
Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning
Weishi Shi (Rochester Institute of Technology) · Xujiang Zhao (The University of Texas at Dallas) · Feng Chen (UT Dallas) · Qi Yu (Rochester Institute of Technology)

岭骑:通过遵循黑森州的特征向量找到不同的解决方案
Ridge Riding: Finding diverse solutions by following eigenvectors of the Hessian
Jack Parker-Holder (University of Oxford) · Cinjon Resnick (NYU) · Luke Metz (Google Brain) · Hengyuan Hu (Facebook) · Adam Lerer (Facebook AI Research) · Alistair Letcher (None) · Alexander Peysakhovich (Facebook) · Aldo Pacchiano (UC Berkeley) · Jakob Foerster (Facebook AI Research)

操作员对策略梯度方法的看法
An Operator View of Policy Gradient Methods
Dibya Ghosh (Google) · Marlos C. Machado (Google Brain) · Nicolas Le Roux (Google Brain)

通过混合整数编程优化拍卖中的上下文底价
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming
Joey Huchette (Rice University) · Haihao Lu (University of Chicago) · Hossein Esfandiari (Google Research) · Vahab Mirrokni (Google Research NYC)

知识丰富领域中眼动数据和言语叙述的动态融合
Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains
Ervine Zheng (Rochester Institute of Technology) · Qi Yu (Rochester Institute of Technology) · Rui Li (Rochester Institute of Technology) · Pengcheng Shi (rit) · Anne Haake (Rochester Institute of Technology)

不断发展的标准化激活层
Evolving Normalization-Activation Layers
Hanxiao Liu (Google Brain) · Andy Brock (DeepMind) · Karen Simonyan (DeepMind) · Quoc V Le (Google)

对数似然比最小化流量:实现稳健和可量化的神经分布对齐
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
Ben Usman (Boston University, Google AI) · Avneesh Sud (Google) · Nick Dufour (Google Research) · Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)

仇恨模因挑战:检测多模态模因中的仇恨言论
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
Douwe Kiela (Facebook AI Research) · Hamed Firooz (Facebook) · Aravind Mohan (Facebook) · Vedanuj Goswami (Facebook) · Amanpreet Singh (Facebook) · Pratik Ringshia (Facebook) · Davide Testuggine (Facebook)

拉格朗日乘数模型的重约束学习
Heavily-Constrained Learning with Lagrange Multiplier Models
Harikrishna Narasimhan (Google Research) · Andrew Cotter (Google) · Yichen Zhou (Google) · Serena Wang (Google, UC Berkeley) · Wenshuo Guo (UC Berkeley)

连续亚模最大化:超越DR亚模
Continuous Submodular Maximization: Beyond DR-Submodularity
Moran Feldman (University of Haifa) · Amin Karbasi (Yale)

具有时间干扰的自适应实验设计:最大似然法
Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach
Peter W Glynn (Stanford University) · Ramesh Johari (Stanford University) · Mohammad Rasouli (Stanford University)

用于标签噪声学习的拓扑过滤器
A Topological Filter for Learning with Label Noise
Pengxiang Wu (Rutgers University) · Songzhu Zheng (Stony Brook University) · Mayank Goswami (Queens College of CUNY) · Dimitris Metaxas (Rutgers University) · Chao Chen (Stony Brook University)

测试错误的交叉验证置信区间
Cross-validation Confidence Intervals for Test Error
Pierre Bayle (Princeton University) · Alexandre Bayle (Harvard University) · Lucas Janson (Harvard University) · Lester Mackey (Microsoft Research)

学习带有有限状态自动机层的图结构
Learning Graph Structure with A Finite-State Automaton Layer
Daniel Johnson (Google Research, Brain Team) · Hugo Larochelle (Google Brain) · Daniel Tarlow (Google Brain)

学习动态信念图以在基于文本的游戏中推广
Learning Dynamic Belief Graphs to Generalize on Text-Based Games
Ashutosh Adhikari (University of Waterloo) · Xingdi Yuan (Microsoft Research) · Marc-Alexandre Côté (Microsoft Research) · Mikuláš Zelinka (Charles University, Faculty of Mathematics and Physics) · Marc-Antoine Rondeau (Microsoft Research) · Romain Laroche (Microsoft Research) · Pascal Poupart (University of Waterloo & RBC Borealis AI) · Jian Tang (Mila) · Adam Trischler (Microsoft) · Will Hamilton (McGill)

论分层强化学习的效率
On Efficiency in Hierarchical Reinforcement Learning
Zheng Wen (DeepMind) · Doina Precup (DeepMind) · Morteza Ibrahimi (DeepMind) · Andre Barreto (DeepMind) · Benjamin Van Roy (Stanford University) · Satinder Singh (DeepMind)

损失景观几何形状和数据相关神经正切核演化的实证研究
An empirical study of loss landscape geometry and evolution of the data-dependent Neural Tangent Kernel
Stanislav Fort (Stanford University / Google Research) · Gintare Karolina Dziugaite (Element AI) · Mansheej Paul (Stanford University) · Sepideh Kharaghani (Element AI) · Daniel Roy (Univ of Toronto & Vector) · Surya Ganguli (Stanford)

使用非合作游戏设计和学习可训练的先验
Designing and Learning Trainable Priors with Non-Cooperative Games
Bruno Lecouat (Inria) · Jean Ponce (Inria) · Julien Mairal (Inria)

RNNs学习过程中随机性与结构之间的相互作用
The interplay between randomness and structure during learning in RNNs
Friedrich Schuessler (Technion) · Francesca Mastrogiuseppe (UCL) · Alexis Dubreuil (ENS) · Srdjan Ostojic (Ecole Normale Superieure) · Omri Barak (Technion - Israeli institute of technology)

知识库查询的忠实嵌入
Faithful Embeddings for Knowledge Base Queries
Haitian Sun (Google Research) · Andrew Arnold (Amazon) · Tania Bedrax Weiss (Google) · Fernando Pereira (Google) · William Cohen (Google AI)

约束对抗网络有效生成结构化对象
Efficient Generation of Structured Objects with Constrained Adversarial Networks
Luca Di Liello (University of Trento) · Pierfrancesco Ardino (University of Trento) · Jacopo Gobbi (University of Trento) · Paolo Morettin (University of Trento) · Stefano Teso (University of Trento) · Andrea Passerini (Università degli Studi di Trento)

复杂网络的节点嵌入和精确的低秩表示
Node Embeddings and Exact Low-Rank Representations of Complex Networks
Sudhanshu Chanpuriya (University of Massachusetts Amherst) · Cameron Musco (Microsoft Research) · Konstantinos Sotiropoulos (Boston University) · Charalampos Tsourakakis (Boston University/ISI foundation)

超参数集成,实现鲁棒性和不确定性量化
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Florian Wenzel (Google Research) · Jasper Snoek (Google Brain) · Dustin Tran (Google Brain) · Rodolphe Jenatton (Google Brain)

通用工具强化学习的变分策略梯度法
Variational Policy Gradient Method for Reinforcement Learning with General Utilities
Junyu Zhang (Princeton University) · Alec Koppel (U.S. Army Research Laboratory) · Amrit Singh Bedi (US Army Research Laboratory) · Csaba Szepesvari (DeepMind / University of Alberta) · Mengdi Wang (Princeton University)

两种时间尺度角色批评方法的有限时间分析
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods
Yue Wu (University of California, Los Angeles) · Weitong ZHANG (University of California, Los Angeles) · Pan Xu (University of California, Los Angeles) · Quanquan Gu (UCLA)

POLY-HOOT:具有非渐近分析的连续空间MDP中的蒙特卡洛规划
POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis
Weichao Mao (University of Illinois Urbana-Champaign) · Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Qiaomin Xie (Cornell University) · Tamer Basar (University of Illinois at Urbana-Champaign)

实例特征分组
Instance-wise Feature Grouping
Aria Masoomi (Northeastern University) · Chieh Wu (Northeastern) · Tingting Zhao (Northeastern University) · Zifeng Wang (Northeastern University) · Peter Castaldi (Brigham Women’s Hospital) · Jennifer Dy (Northeastern University)

通过稳健的排名第一矩阵完成对抗性众包
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion
Qianqian Ma (Boston University) · Alex Olshevsky (Boston University)

关于大型非线性模型的线性:何时和为什么正切核常数
On the linearity of large non-linear models: when and why is the tangent kernel constant
Chaoyue Liu (The Ohio State University) · Libin Zhu (The Ohio State University) · Mikhail Belkin (UC San Diego)

通过子空间扩散解缠结
Disentangling by Subspace Diffusion
David Pfau (DeepMind) · Irina Higgins (DeepMind) · Alex Botev () · Sébastien Racanière (Google DeepMind)

深度自动调制器
Deep Automodulators
Ari Heljakka (Aalto University) · Yuxin Hou (Aalto University) · Juho Kannala (Aalto University) · Arno Solin (Aalto University)

重量轻且深度隔离的神经网络
Neural Networks with Small Weights and Depth-Separation Barriers
Gal Vardi (Weizmann Institute of Science) · Ohad Shamir (Weizmann Institute of Science)

COOT:用于视频文本表示学习的协作式分层变压器
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
Mohammadreza Zolfaghari (University of Freiburg) · Simon Ging (Uni Freiburg) · Hamed Pirsiavash (University of Maryland, Baltimore County) · Thomas Brox (University of Freiburg)

时差和Q学习可以学习表示形式吗?平均场理论
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
Yufeng Zhang (Northwestern University) · Qi Cai (Northwestern University) · Zhuoran Yang (Princeton) · Yongxin Chen (Georgia Institute of Technology) · Zhaoran Wang (Northwestern University)

价值驱动的事后建模
Value-driven Hindsight Modelling
Arthur Guez (DeepMind) · Fabio Viola (DeepMind) · Theophane Weber (DeepMind) · Lars Buesing (Google DeepMind) · Steven Kapturowski (Deepmind) · Doina Precup (DeepMind) · David Silver (DeepMind) · Nicolas Heess (Google DeepMind)

在阴影中行走:关于约束最小化的下降方向的新观点
Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization
Hassan Mortagy (Georgia Institute of Technology) · Swati Gupta (Georgia Institute of Technology) · Sebastian Pokutta (Zuse Institute Berlin)

机器学习建议的多店滑雪租赁在线算法
Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice
Shufan Wang (Binghamton University-SUNY) · Jian Li (Binghamton University-SUNY ) · Shiqiang Wang (IBM Research)

具有神经ODE的半马尔可夫决策过程的基于模型的强化学习
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
Jianzhun Du (Harvard University) · Joseph Futoma (Harvard University) · Finale Doshi-Velez (Harvard)

GradAug:用于深度神经网络的新正则化方法
GradAug: A New Regularization Method for Deep Neural Networks
TAOJIANNAN YANG (University of North Carolina at Charlotte) · Sijie Zhu (University of North Carolina at Charlotte) · Chen Chen (University of North Carolina at Charlotte)

卷积神经网络中纹理偏差的起源和盛行
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks
Katherine Hermann (Stanford University) · Ting Chen (Google) · Simon Kornblith (Google Brain)

无限理性计划代理人的在线贝叶斯目标推断
Online Bayesian Goal Inference for Boundedly Rational Planning Agents
Tan Zhi-Xuan (Massachusetts Institute of Technology) · Jordyn Mann (Massachusetts Institute of Technology) · Tom Silver (MIT) · Josh Tenenbaum (MIT) · Vikash Mansinghka (Massachusetts Institute of Technology)

将对抗性强健的学习减少为非稳健的PAC学习
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
Omar Montasser (Toyota Technological Institute at Chicago) · Steve Hanneke (Toyota Technological Institute at Chicago) · Nati Srebro (TTI-Chicago)

模型说明的调试测试
Debugging Tests for Model Explanations
Julius Adebayo (MIT) · Michael Muelly (Stanford University) · Ilaria Liccardi (MIT) · Been Kim (Google)

通过部分参数对任务进行自适应的元学习的收敛
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters
Kaiyi Ji (The Ohio State University) · Jason Lee (Princeton University) · Yingbin Liang (The Ohio State University) · H. Vincent Poor (Princeton University)

学习负担​​能力景观以在3D环境中进行交互探索
Learning Affordance Landscapes for Interaction Exploration in 3D Environments
Tushar Nagarajan (UT Austin) · Kristen Grauman (University of Texas at Austin)

用于训练基于分数的生成模型的改进技术
Improved Techniques for Training Score-Based Generative Models
Yang Song (Stanford University) · Stefano Ermon (Stanford)

学习激励其他学习代理
Learning to Incentivize Other Learning Agents
Jiachen Yang (Georgia Institute of Technology) · Ang Li (DeepMind, Mountain View) · Mehrdad Farajtabar (DeepMind) · Peter Sunehag (Google - DeepMind) · Edward Hughes (DeepMind) · Hongyuan Zha (Georgia Tech)

异步Q学习的样本复杂性:更清晰的分析和方差减少
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction
Gen Li (Tsinghua University) · Yuting Wei (Carnegie Mellon University) · Yuejie Chi (CMU) · Yuantao Gu (Tsinghua University) · Yuxin Chen (Princeton University)

具有理论保证的稀疏深度学习的有效变分推理
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee
Jincheng Bai (Purdue University) · Qifan Song (Purdue University ) · Guang Cheng (Purdue University)

通过无人监管的环境设计实现了紧急情况的复杂性和零散转移
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Michael Dennis (University of California Berkeley) · Natasha Jaques (MIT) · Eugene Vinitsky (UC Berkeley) · Alexandre Bayen (UC Berkeley) · Stuart Russell (UC Berkeley) · Andrew Critch (UC Berkeley) · Sergey Levine (UC Berkeley)

傅里叶稀疏杠杆得分和近似内核学习
Fourier Sparse Leverage Scores and Approximate Kernel Learning
Tamas Erdelyi (Texas A&M University) · Cameron Musco (Microsoft Research) · Christopher Musco (New York University)

运动修剪:微调的自适应稀疏
Movement Pruning: Adaptive Sparsity by Fine-Tuning
Victor Sanh (Hugging Face 🤗) · Thomas Wolf (Hugging Face) · Alexander Rush (Cornell University)

自适应重要性采样,用于有限和的优化和减小步长的采样
Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes
Ayoub El Hanchi (McGill University) · David Stephens (McGill University)

图网的主要邻域聚合
Principal Neighbourhood Aggregation for Graph Nets
Gabriele Corso (University of Cambridge) · Luca Cavalleri (University of Cambridge) · Dominique Beaini (Invivo AI) · Pietro Liò (University of Cambridge) · Petar Veličković (DeepMind)

学习稀疏规则的信息理论极限
Information theoretic limits of learning a sparse rule
Clément Luneau (École Polytechnique Fédérale de Lausanne) · jean barbier (EPFL) · Nicolas Macris (EPFL)

向私人和公共人口混合学习
Learning from Mixtures of Private and Public Populations
Raef Bassily (The Ohio State University) · Shay Moran (Google AI Princeton) · Anupama Nandi (The Ohio State University)

关于热启动神经网络训练
On Warm-Starting Neural Network Training
Jordan Ash (Microsoft Research) · Ryan Adams (Princeton University)

视觉和语言表示学习的大规模对抗训练
Large-Scale Adversarial Training for Vision-and-Language Representation Learning
Zhe Gan (Microsoft) · Yen-Chun Chen (Microsoft) · Linjie Li (Microsoft) · Chen Zhu (University of Maryland) · Yu Cheng (Microsoft) · Jingjing Liu (Microsoft)

对平均随机梯度下降进行去偏以处理缺失值
Debiasing Averaged Stochastic Gradient Descent to handle missing values
Aude Sportisse (Sorbonne University, Ecole Polytechnique) · Claire Boyer (LPSM, Sorbonne Université) · Aymeric Dieuleveut (Ecole Polytechnique, IPParis) · Julie Josses (CMAP / CNRS)

通过可允许的神经启发式学习可区分的程序
Learning Differentiable Programs with Admissible Neural Heuristics
Ameesh Shah (UC Berkeley) · Eric Zhan (Caltech) · Jennifer Sun (Caltech) · Abhinav Verma (Rice University) · Yisong Yue (Caltech) · Swarat Chaudhuri (The University of Texas at Austin)

高斯混合分类中随机梯度下降的动态平均场理论
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification
Francesca Mignacco (IPhT, CEA Saclay) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL) · Pierfrancesco Urbani (Institut de Physique Théorique) · Lenka Zdeborová (University Paris-Saclay & EPFL)

突破性的可逆性加速了Langevin动力学,实现了非凸优化
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization
Xuefeng GAO (The Chinese University of Hong Kong) · Mert Gurbuzbalaban (Rutgers) · Lingjiong Zhu (Florida State University)

随时间推移对深度逆模型进行基准测试以及神经伴随方法
Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method
Simiao Ren (Duke University) · Willie Padilla (Duke University) · Jordan Malof (Duke University)

分布式最近邻分类的统计保证
Statistical Guarantees of Distributed Nearest Neighbor Classification
Jiexin Duan (Purdue University) · Xingye Qiao (Binghamton University) · Guang Cheng (Purdue University)

在野外检测手并识别身体接触
Detecting Hands and Recognizing Physical Contact in the Wild
Supreeth Narasimhaswamy (Stony Brook University) · Trung Nguyen (VinAI) · Minh Hoai Nguyen (Stony Brook University)

一般总和部分可观马尔可夫博弈中共享均衡的标定
Calibration of Shared Equilibria in General Sum Partially Observable Markov Games
Nelson Vadori (J.P. Morgan AI Research) · Sumitra Ganesh (JPMorgan - AI Research) · Prashant Reddy (JP Morgan) · Manuela Veloso (J.P. Morgan AI Research )

使用因果中介分析研究语言模型中的性别偏见
Investigating Gender Bias in Language Models Using Causal Mediation Analysis
Jesse Vig (Salesforce) · Sebastian Gehrmann (Harvard University) · Yonatan Belinkov (Technion) · Sharon Qian (Harvard) · Daniel Nevo (Tel Aviv University) · Yaron Singer (Harvard University) · Stuart Shieber (Harvard University)

有条件元学习在有偏正则化和微调中的优势
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning
Giulia Denevi (University College of London) · Massimiliano Pontil (IIT & UCL) · Carlo Ciliberto (Imperial College London)

最短路径路由的自适应探测策略
Adaptive Probing Policies for Shortest Path Routing
Aditya Bhaskara (University of Utah) · Sreenivas Gollapudi (Google Research) · Kostas Kollias (Google Research) · Kamesh Munagala (Duke University)

学习丰富的排名
Learning Rich Rankings
Arjun Seshadri (Stanford University) · Stephen Ragain (Twitter) · Johan Ugander (Stanford University)

在线和差异化私人学习的平滑分析
Smoothed Analysis of Online and Differentially Private Learning
Nika Haghtalab (Cornell University) · Tim Roughgarden (Columbia University) · Abhishek Shetty (Cornell University)

使用自我监督对象建议的具有组合概括性的学习策略
Learning Policy with Compositional Generalizability using Self-Supervised Object Proposals
Jiayuan Gu (University of California, San Diego) · Tongzhou Mu (University of California, San Diego) · Zhiwei Jia (University of California, San Diego) · Hao Tang (Shanghai Jiao Tong University) · Hao Su (UCSD)

通过加速梯度修剪实现重尾噪声的随机优化
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping
Eduard Gorbunov (Moscow Institute of Physics and Technology) · Marina Danilova (ICS RAS) · Alexander Gasnikov (MIPT & HSE)

具有连续动作的高效上下文强盗
Efficient Contextual Bandits with Continuous Actions
Maryam Majzoubi (NYU Tandon) · Chicheng Zhang (University of Arizona) · Rajan Chari (Microsoft) · Akshay Krishnamurthy (Microsoft) · John Langford (Microsoft Research New York) · Aleksandrs Slivkins (Microsoft Research)

从树到连续嵌入再到背面:双曲层次聚类
From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering
Ines Chami (Stanford University) · Albert Gu (Stanford) · Vaggos Chatziafratis (Stanford University, California) · Christopher Ré (Stanford)

关于对抗训练的失败态势:识别挑战以及如何克服挑战
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
Chen Liu (EPFL) · Mathieu Salzmann (EPFL) · Tao Lin (EPFL) · Ryota Tomioka (Microsoft Research Cambridge) · Sabine Süsstrunk (EPFL)

Graphon神经网络和图神经网络的可传递性
Graphon Neural Networks and the Transferability of Graph Neural Networks
Luana Ruiz (University of Pennsylvania) · Luiz Chamon (University of Pennsylvania) · Alejandro Ribeiro (University of Pennsylvania)

在线贝叶斯说服
Online Bayesian Persuasion
Matteo Castiglioni (Politecnico di Milano) · Andrea Celli (Politecnico di Milano) · Alberto Marchesi (Politecnico di Milano) · Nicola Gatti (Politecnico di Milano)

线性收敛误差补偿SGD
Linearly Converging Error Compensated SGD
Eduard Gorbunov (Moscow Institute of Physics and Technology) · Dmitry Koralev (KAUST) · Dmitry Makarenko (MIPT) · Peter Richtarik (KAUST)

可分解图卷积网络
Factorizable Graph Convolutional Networks
Yiding Yang (Stevens Institute of Technology) · Zunlei Feng (Zhejiang University) · Mingli Song (Zhejiang University) · Xinchao Wang (Stevens Institute of Technology)

使用图表示学习处理缺失数据
Handling Missing Data with Graph Representation Learning
Jiaxuan You (Stanford University) · Xiaobai Ma (Stanford University) · Yi Ding (Stanford University) · Mykel J Kochenderfer (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

人群匹配的分布匹配
Distribution Matching for Crowd Counting
Boyu Wang (Stony Brook University) · Huidong Liu (Stony Brook University) · Dimitris Samaras (Stony Brook University) · Minh Hoai Nguyen (Stony Brook University)

利用增强数据进行强化学习
Reinforcement Learning with Augmented Data
Misha Laskin (UC Berkeley) · Kimin Lee (UC Berkeley) · Adam Stooke (UC Berkeley) · Lerrel Pinto (New York University) · Pieter Abbeel (UC Berkeley & covariant.ai) · Aravind Srinivas (UC Berkeley)

MDP中增量式自主勘探的改进的样本复杂度
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs
Jean Tarbouriech (Facebook AI Research Paris & Inria Lille) · Matteo Pirotta (Facebook AI Research) · Michal Valko (DeepMind Paris and Inria Lille - Nord Europe) · Alessandro Lazaric (Facebook Artificial Intelligence Research)

精确计算ReLU网络的本地Lipschitz常数
Exactly Computing the Local Lipschitz Constant of ReLU Networks
Matt Jordan (UT Austin) · Alexandros Dimakis (University of Texas, Austin)

可解释的多时标模型,用于预测对连续自然语音的功能磁共振成像反应
Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech
Shailee Jain (The University of Texas at Austin) · Vy Vo (Intel Corporation) · Shivangi Mahto (The University of Texas at Austin) · Amanda LeBel (The University of Texas at Austin) · Javier Turek (Intel Labs) · Alexander Huth (The University of Texas at Austin)

广义形式相关平衡的无悔学习动力学
No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
Andrea Celli (Politecnico di Milano) · Alberto Marchesi (Politecnico di Milano) · Gabriele Farina (Carnegie Mellon University) · Nicola Gatti (Politecnico di Milano)

EvolveGraph:具有动态关系推理的多Agent轨迹预测
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
Jiachen Li (University of California, Berkeley) · Fan Yang (University of California, Berkeley) · Masayoshi Tomizuka (University of California, Berkeley) · Chiho Choi (Honda Research Institute US)

发现签名网络中的冲突组
Discovering conflicting groups in signed networks
Ruo-Chun Tzeng (KTH) · Bruno Ordozgoiti (Aalto University) · Aristides Gionis (KTH Royal Institute of Technology)

非光滑凸损失下随机梯度下降的稳定性
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
Raef Bassily (The Ohio State University) · Vitaly Feldman (Google Brain) · Cristobal Guzman (PUC-Chile) · Kunal Talwar (Google)

在线控制的几何探索
Geometric Exploration for Online Control
Orestis Plevrakis (Princeton University) · Elad Hazan (Princeton University)

具有最佳运输的可微分Top-k
Differentiable Top-k with Optimal Transport
Yujia Xie (Georgia Institute of Technology) · Hanjun Dai (Google Brain) · Minshuo Chen (Georgia Tech) · Bo Dai (Google Brain) · Tuo Zhao (Gatech) · Hongyuan Zha (Georgia Tech) · Wei Wei (Google Inc.) · Tomas Pfister (Google)

自动聚焦的Oracle用于基于模型的设计
Autofocused oracles for model-based design
Clara Fannjiang (UC Berkeley) · Jennifer Listgarten (UC Berkeley)

看,听,探索:通过视听协会的好奇心
See, Hear, Explore: Curiosity via Audio-Visual Association
Victoria Dean (Carnegie Mellon University) · Shubham Tulsiani (Facebook AI Research) · Abhinav Gupta (Facebook AI Research/CMU)

拜占庭式弹性分布式多任务学习
Byzantine Resilient Distributed Multi-Task Learning
JIANI LI (Vanderbilt University) · Waseem Abbas (Vanderbilt University) · Xenofon Koutsoukos (Vanderbilt University)

使用保证金排名下的区域识别标签错误的数据
Identifying Mislabeled Data using the Area Under the Margin Ranking
Geoff Pleiss (Columbia University) · Tianyi Zhang (Cornell University & ASAPP Research) · Ethan Elenberg (ASAPP) · Kilian Weinberger (Cornell University / ASAPP Research)

概率公平聚类
Probabilistic Fair Clustering
Seyed Esmaeili (University of Maryland, College Park) · Brian Brubach (University of Maryland) · Leonidas Tsepenekas (University of Maryland) · John Dickerson (University of Maryland)

不精确模型的在线非凸优化
Online Non-Convex Optimization with Inexact Models
Amélie Héliou (Criteo AI Lab) · Matthieu Martin (Criteo) · Panayotis Mertikopoulos (CNRS (French National Center for Scientific Research)) · Thibaud J Rahier (INRIA)

神经网络中的深度不确定度
Depth Uncertainty in Neural Networks
Javier Antoran (University of Cambridge) · James U Allingham (University of Cambridge) · José Miguel Hernández-Lobato (University of Cambridge)

为什么深度残差网络比深度前馈网络具有更好的通用性?—神经正切核的观点
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? — A Neural Tangent Kernel Perspective
Kaixuan Huang (Princeton University) · Yuqing Wang (Georgia Institute of Technology) · Molei Tao (Georgia Institute of Technology) · Tuo Zhao (Gatech)

通过正则拉格朗日法进行政策外评估
Off-Policy Evaluation via the Regularized Lagrangian
Mengjiao Yang (Google) · Ofir Nachum (Google Brain) · Bo Dai (Google Brain) · Lihong Li (Google Research) · Dale Schuurmans (Google Brain & University of Alberta)

基于能量分配匹配的严格批量模仿学习
Strictly Batch Imitation Learning by Energy-based Distribution Matching
Daniel Jarrett (University of Cambridge) · Ioana Bica (University of Oxford) · Mihaela van der Schaar (University of Cambridge)

按功能等级划分的简单,可扩展的稀疏k均值
Simple, Scalable Sparse k-means by Feature Ranking
Zhiyue Zhang (Duke University) · Kenneth Lange (UCLA) · Jason Xu (Duke University)

快速矩阵平方根在高斯过程和贝叶斯优化中的应用
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Geoff Pleiss (Columbia University) · Martin Jankowiak (Uber AI Labs) · David Eriksson (Facebook) · Anil Damle (Cornell University) · Jacob Gardner (University of Pennsylvania)

编码的顺序矩阵乘法,可缓解流浪汉
Coded Sequential Matrix Multiplication For Straggler Mitigation
Nikhil Krishnan Muralee Krishnan (University of Toronto) · Seyederfan Hosseini (University of Toronto) · Ashish Khisti (University of Toronto)

使用pi-VAE学习高维神经活动的可识别和可解释的潜在模型
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE
Ding Zhou (Columbia University) · Xue-Xin Wei (University of Pennsylvania)

通过维度的好处驯服离散集成
Taming Discrete Integration via the Boon of Dimensionality
Jeffrey Dudek (Rice University) · Dror Fried (The Open University of Israel) · Kuldeep S Meel (National University of Singapore)

距离编码-设计更强大的GNN以进行结构表示学习
Distance Encoding – Design Provably More Powerful GNNs for Structural Representation Learning
Pan Li (Stanford University - Purdue University) · Yanbang Wang (Stanford University) · Hongwei Wang (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

具有梯度下降的单个神经元的不可知论学习
Agnostic Learning of a Single Neuron with Gradient Descent
Spencer Frei (UCLA) · Yuan Cao (UCLA) · Quanquan Gu (UCLA)

图神经网络的设计空间
Design Space for Graph Neural Networks
Jiaxuan You (Stanford University) · Zhitao Ying (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

对抗示例游戏
Adversarial Example Games
Joey Bose (McGill/MILA) · Gauthier Gidel (Mila) · Hugo Berard (MILA) · Andre Cianflone (Mila/McGill) · Pascal Vincent (Facebook and U. Montreal) · Simon Lacoste-Julien (Mila, Université de Montréal & SAIL Montreal) · Will Hamilton (McGill)

数据高效GAN培训的差异化增强
Differentiable Augmentation for Data-Efficient GAN Training
Shengyu Zhao (Tsinghua University) · Zhijian Liu (MIT) · Ji Lin (MIT) · Jun-Yan Zhu (MIT) · Song Han (MIT)

迈向模仿学习的根本极限
Toward the Fundamental Limits of Imitation Learning
Nived Rajaraman (University of California, Berkeley) · Lin Yang (UCLA) · Jiantao Jiao (University of California, Berkeley) · Kannan Ramchandran (UC Berkeley)

WoodFisher:神经网络压缩的有效二阶逼近
WoodFisher: Efficient Second-Order Approximation for Neural Network Compression
Sidak Pal Singh (EPFL) · Dan Alistarh (IST Austria & Neural Magic Inc.)

关于转移学习的理论:任务多样性的重要性
On the Theory of Transfer Learning: The Importance of Task Diversity
Nilesh Tripuraneni (UC Berkeley) · Michael Jordan (UC Berkeley) · Chi Jin (Princeton University)

关于深层网络训练的动力学
On the training dynamics of deep networks with
L
2
正则化
regularization
Aitor Lewkowycz (Google) · Guy Gur-Ari (Google)

与介入数据的因果发现
Differentiable Causal Discovery from Interventional Data
Philippe Brouillard (Mila, Université de Montréal) · Sébastien Lachapelle (Mila) · Alexandre Lacoste (Element AI) · Simon Lacoste-Julien (Mila, Université de Montréal & SAIL Montreal) · Alexandre Drouin (Element AI)

单眼深度预测的有目标对抗性扰动
Targeted Adversarial Perturbations for Monocular Depth Prediction
Alex Wong (University of Los Angeles, California) · Safa Cicek (UCLA) · Stefano Soatto (UCLA)

神经网络健壮性的因果观点
A Causal View on Robustness of Neural Networks
Cheng Zhang (Microsoft Research, Cambridge, UK) · Kun Zhang (CMU) · Yingzhen Li (Microsoft Research Cambridge)

高维截断线性回归
Truncated Linear Regression in High Dimensions
Constantinos Daskalakis (MIT) · Dhruv Rohatgi (Massachusetts Institute of Technology) · Emmanouil Zampetakis (MIT)

通过遗憾最小化实现在线不可知论
Online Agnostic Boosting via Regret Minimization
Nataly Brukhim (Princeton University) · Xinyi Chen (Princeton University) · Elad Hazan (Princeton University) · Shay Moran (Google AI Princeton)

连续学习叠加中的超级掩码
Supermasks in Superposition for Continual Learning
Mitchell Wortsman (University of Washington, Allen Institute for Artificial Intelligence) · Vivek Ramanujan (Allen Institute for Artificial Intelligence) · Rosanne Liu (ML Collective) · Aniruddha Kembhavi (Allen Institute for Artificial Intelligence (AI2)) · Mohammad Rastegari (University of Washington) · Jason Yosinski (Uber AI; Recursion) · Ali Farhadi (University of Washington)

从经过验证的培训数据中获得最佳学习
Optimal Learning from Verified Training Data
Nicholas Bishop (University of Southampton) · Long Tran-Thanh (University of Warwick) · Enrico Gerding (university of Southampton)

单调神经网络的反例指导学习
Counterexample-Guided Learning of Monotonic Neural Networks
Aishwarya Sivaraman (UCLA) · Golnoosh Farnadi (Mila) · Todd Millstein (UCLA) · Guy Van den Broeck (UCLA)

COBE:叙述性教学视频中的上下文化对象嵌入
COBE: Contextualized Object Embeddings from Narrated Instructional Video
Gedas Bertasius (Facebook Research) · Lorenzo Torresani (Facebook AI)

随机改组并不总是更好
Random Reshuffling is Not Always Better
Christopher De Sa (Cornell)

学习可组合能量替代品以减少PDE订单
Learning Composable Energy Surrogates for PDE Order Reduction
Alex Beatson (Princeton University) · Jordan Ash (Microsoft Research) · Geoffrey Roeder (Princeton University X, Alphabet Inc.) · Tianju Xue (Princeton University) · Ryan Adams (Princeton University)

学习基于波的成像的几何
Learning the Geometry of Wave-Based Imaging
Konik R Kothari (University of Illinois at Urbana Champaign) · Maarten de Hoop (Rice University) · Ivan Dokmanić (University of Basel)

强大的联合学习:仿射分布转移的情况
Robust Federated Learning: The Case of Affine Distribution Shifts
Amirhossein Reisizadeh (UC Santa Barbara) · Farzan Farnia (Stanford University) · Ramtin Pedarsani (UC Santa Barbara) · Ali Jadbabaie (MIT)

具有组合动作的强化学习:车辆路径的应用
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing
Arthur Delarue (MIT) · Ross Anderson (Google Research) · Christian Tjandraatmadja (Google)

使用符号矩阵进行快速几何学习
Fast geometric learning with symbolic matrices
Jean Feydy (École Normale Supérieure) · Joan Glaunès (Université Paris 5) · Benjamin Charlier (University of Montpellier) · Michael Bronstein (Imperial College London / Twitter)

MOPO:基于模型的脱机策略优化
MOPO: Model-based Offline Policy Optimization
Tianhe Yu (Stanford University) · Garrett W. Thomas (Stanford University) · Lantao Yu (Stanford University) · Stefano Ermon (Stanford) · James Zou (Stanford University) · Sergey Levine (UC Berkeley) · Chelsea Finn (Stanford) · Tengyu Ma (Stanford University)

从预测到决策:使用前瞻性正则化
From Predictions to Decisions: Using Lookahead Regularization
Nir Rosenfeld (Harvard University) · Anna Hilgard (Harvard University) · Sai Srivatsa Ravindranath (Harvard University) · David Parkes (Harvard University)

无监督架构表示学习是否有助于神经架构搜索?
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?
Shen Yan (Michigan State University) · Yu Zheng (Michigan State University) · Wei Ao (Michigan State University) · Xiao Zeng (Michigan State University) · Mi Zhang (Michigan State University)

通过视觉和触摸进行3D形状重建
3D Shape Reconstruction from Vision and Touch
Edward Smith (McGill University) · Roberto Calandra (Facebook AI Research) · Adriana Romero (FAIR) · Georgia Gkioxari (Facebook) · David Meger (McGill University) · Jitendra Malik (University of California at Berkley) · Michal Drozdzal (FAIR)

雷声:稀疏学习的快速坐标选择解决方案
Thunder: a Fast Coordinate Selection Solver for Sparse Learning
Shaogang Ren (Baidu Research, USA) · Weijie Zhao (Baidu Research) · Ping Li (Baidu Research USA)

减少方差的非政策性TDC学习:非渐近收敛分析
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis
Shaocong Ma (University of Utah) · Yi Zhou (University of Utah) · Shaofeng Zou (University at Buffalo, the State University of New York)

指针图网络
Pointer Graph Networks
Petar Veličković (DeepMind) · Lars Buesing (Google DeepMind) · Matthew Overlan (DeepMind) · Razvan Pascanu (Google DeepMind) · Oriol Vinyals (Google DeepMind) · Charles Blundell (DeepMind)

具有生成先验的压缩感知的恒定扩展就足够了
Constant-Expansion Suffices for Compressed Sensing with Generative Priors
Constantinos Daskalakis (MIT) · Dhruv Rohatgi (Massachusetts Institute of Technology) · Emmanouil Zampetakis (MIT)

从视觉场景中学习物理图表示
Learning Physical Graph Representations from Visual Scenes
Daniel Bear (Stanford University) · Chaofei Fan (Stanford) · Damian Mrowca (Stanford University) · Yunzhu Li (MIT) · Seth Alter (MIT) · Aran Nayebi (Stanford University) · Jeremy Schwartz (MIT) · Li Fei-Fei (Stanford University & Google) · Jiajun Wu (Google) · Josh Tenenbaum (MIT) · Daniel Yamins (Stanford University)

为什么规范化流无法检测到分布不足的数据
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Polina Kirichenko (New York University) · Pavel Izmailov (New York University) · Andrew Gordon Wilson (New York University)

来自多个大脑区域的神经人口数据的混合共享成分分析
Demixed shared component analysis of neural population data from multiple brain areas
Yu Takagi (University of Oxford) · Steven Kennerley (“Institute of Neurology - Sobell Dept., University College of London”) · Jun-ichiro Hirayama (AIST) · Laurence Hunt (University of Oxford)

一个好的起始点值多少个样本?
How many samples is a good initial point worth?
Jialun Zhang (University of Illinois Urbana Champaign) · Richard Zhang (UIUC)

学习记忆有效的稳定线性动力学系统进行预测和控制
Learning Memory-Efficient Stable Linear Dynamical Systems for Prediction and Control
Georgios Mamakoukas (Northwestern University) · Orest Xherija (University of Chicago) · Todd Murphey (Northwestern Univ.)

通过后悔最小化,简化了稳健而重尾的均值估计
Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization
Sam Hopkins () · Jerry Li (Microsoft) · Fred Zhang (UC Berkeley)

学习增强型在线算法的最佳鲁棒一致性一致性折衷
Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms
Alexander Wei (Harvard University) · Fred Zhang (UC Berkeley)

集群式联合学习的有效框架
An Efficient Framework for Clustered Federated Learning
Avishek Ghosh (University of California, Berkeley) · Jichan Chung (University of California, Berkeley) · Dong Yin (DeepMind) · Kannan Ramchandran (UC Berkeley)

简单神经网络中的复杂动力学:了解相位检索中的梯度流
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval
Stefano Sarao Mannelli (Institut de Physique Théorique) · Giulio Biroli (ENS) · Chiara Cammarota (King’s College London) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL) · Pierfrancesco Urbani (Institut de Physique Théorique) · Lenka Zdeborová (University Paris-Saclay & EPFL)

马尔可夫分数攀升:KL(p || q)的变分推断
Markovian Score Climbing: Variational Inference with KL(p||q)
Christian Naesseth (Columbia University) · Fredrik Lindsten (Linköping University) · David Blei (Columbia University)

用于稳健估计和自动结构发现的多任务可加模型
Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery
Yingjie Wang (Huazhong Agricultural University) · Hong Chen (University of Texas at Arlington) · Feng Zheng (SUSTech) · Chen Xu (University of Ottawa) · Tieliang Gong (University of Ottawa) · Yanhong Chen ( Chinese Academy of Sciences)

理想:不精确的分散式加速增强拉格朗日方法
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
Yossi Arjevani (NYU) · Joan Bruna (NYU) · Bugra Can (Rutgers University) · Mert Gurbuzbalaban (Rutgers) · Stefanie Jegelka (MIT) · Hongzhou Lin (MIT)

对抗封锁匪
Adversarial Blocking Bandits
Nicholas Bishop (University of Southampton) · Hau Chan (University of Nebraska-Lincoln) · Debmalya Mandal (Columbia University) · Long Tran-Thanh (University of Warwick)

具有二次激活函数的浅层神经网络的优化和推广
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions
Stefano Sarao Mannelli (Institut de Physique Théorique) · Eric Vanden-Eijnden (New York University) · Lenka Zdeborová (University Paris-Saclay & EPFL)

贝叶斯神经网络权重的分层高斯过程先验
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Theofanis Karaletsos (Uber AI Labs) · Thang Bui (Uber AI / University of Sydney)

神经网络修剪中的广义稳定性权衡
The Generalization-Stability Tradeoff In Neural Network Pruning
Brian Bartoldson (Lawrence Livermore National Laboratory) · Ari Morcos (Facebook AI Research) · Adrian Barbu (Florida State University, USA) · Gordon Erlebacher (Florida State University)

兴奋性和抑制性神经元的最佳平衡尖峰网络的极小极大动力学
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons
Qianyi Li (Harvard University) · Cengiz Pehlevan (Harvard University)

沉默的锥体:通过本地化进行语音分离
The Cone of Silence: Speech Separation by Localization
Teerapat Jenrungrot (University of Washington) · Vivek Jayaram (University of Washington) · Steve Seitz (University of Washington) · Ira Kemelmacher-Shlizerman (University of Washington)

异构处理效果的极小极大最优非参数估计
Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects
Zijun Gao (Stanford University) · Yanjun Han (Stanford University)

带有噪声,混沌和延迟的平衡神经网络中的预测编码
Predictive coding in balanced neural networks with noise, chaos and delays
Jonathan Kadmon (Stanford University) · Jonathan Timcheck (Stanford University) · Surya Ganguli (Stanford)

重新考虑反事实推理的生成目标
Reconsidering Generative Objectives For Counterfactual Reasoning
Danni Lu (Virginia Tech) · Chenyang Tao (Duke University) · Junya Chen (Duke U) · Fan Li (Duke University) · Feng Guo (Virginia Tech) · Lawrence Carin (Duke University)

批匪的推断
Inference for Batched Bandits
Kelly W Zhang (Harvard University) · Lucas Janson (Harvard University) · Susan Murphy (Harvard University)

重新思考预训练和自我训练
Rethinking Pre-training and Self-training
Barret Zoph (Google Brain) · Golnaz Ghiasi (Google) · Tsung-Yi Lin (Google Brain) · Yin Cui (Google) · Hanxiao Liu (Google Brain) · Ekin Dogus Cubuk (Google Brain) · Quoc V Le (Google)

非凸度量学习的梯度下降泛化界
Generalization Bound of Gradient Descent for Non-Convex Metric Learning
MINGZHI DONG (University College London) · Xiaochen Yang (University College London) · Rui Zhu (City, University of London) · Yujiang Wang (Imperial College London) · Jing-Hao Xue (University College London)

利用球优化Oracle进行加速
Acceleration with a Ball Optimization Oracle
Yair Carmon (Stanford University) · Arun Jambulapati (Stanford University) · Qijia Jiang (Stanford University) · Yujia Jin (Stanford University) · Yin Tat Lee (UW) · Aaron Sidford (Stanford) · Kevin Tian (Stanford University)

重叠群体的公平;概率论
Fairness with Overlapping Groups; a Probabilistic Perspective
Forest Yang (UC Berkeley) · Mouhamadou M Cisse (KAUST) · Oluwasanmi Koyejo (UIUC)

通过价值分歧实现自动课程学习
Automatic Curriculum Learning through Value Disagreement
Yunzhi Zhang (Berkeley Artificial Intelligence Research Lab) · Pieter Abbeel (UC Berkeley & covariant.ai) · Lerrel Pinto (New York University)

非平稳环境中策略优化的动态遗憾
Dynamic Regret of Policy Optimization in Non-stationary Environments
Yingjie Fei (Cornell University) · Zhuoran Yang (Princeton) · Zhaoran Wang (Northwestern University) · Qiaomin Xie (Cornell University)

确保培训数据以外的公平
Ensuring fairness beyond the training data
Debmalya Mandal (Columbia University) · Samuel Deng (Columbia University) · Suman Jana (Columbia University) · Jeannette Wing (Columbia University) · Daniel Hsu (Columbia University)

标签偏移估计的统一视图
A Unified View of Label Shift Estimation
Saurabh Garg (CMU) · Yifan Wu (Carnegie Mellon University) · Sivaraman Balakrishnan (CMU) · Zachary Lipton (Carnegie Mellon University)

DisCor:通过分布校正的强化学习中的校正反馈
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction
Aviral Kumar (UC Berkeley) · Abhishek Gupta (University of California, Berkeley) · Sergey Levine (UC Berkeley)

图卷积网络的奖励传播
Reward Propagation Using Graph Convolutional Networks
Martin Klissarov (Mila/McGill University) · Doina Precup (McGill University / Mila / DeepMind Montreal)

FLAMBE:低等级MDP的结构复杂性和表示学习
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
Alekh Agarwal (Microsoft Research) · Sham Kakade (University of Washington & Microsoft Research) · Akshay Krishnamurthy (Microsoft) · Wen Sun (Microsoft Research NYC)

神经符号强化学习与正式验证的探索
Neurosymbolic Reinforcement Learning with Formally Verified Exploration
Greg Anderson (University of Texas at Austin) · Abhinav Verma (Rice University) · Isil Dillig (UT Austin) · Swarat Chaudhuri (The University of Texas at Austin)

通过随机签到扩大隐私
Privacy Amplification via Random Check-Ins
Borja Balle (Amazon) · Peter Kairouz (Google) · Brendan McMahan (Google) · Om Thakkar (Google) · Abhradeep Thakurta (Google)

从训练数据中学习神经网络的不变性
Learning Invariances in Neural Networks from Training Data
Gregory Benton (New York University) · Marc Finzi (New York University) · Pavel Izmailov (New York University) · Andrew Gordon Wilson (New York University)

几何全向布尔张量分解
Geometric All-way Boolean Tensor Decomposition
Changlin Wan (Department of Electrical and Computer Engineering, Purdue University) · Wennan Chang (Department of Electrical and Computer Engineering, Purdue University) · Tong Zhao (Amazon) · Sha Cao (Indiana University) · Chi Zhang (Indiana University School of Medicine)

通过考夫曼算子理论优化神经网络
Optimizing Neural Networks via Koopman Operator Theory
Akshunna Dogra (Harvard University) · William T Redman (UC Santa Barbara)

通过无参数在线学习获得更好的全矩阵遗憾
Better Full-Matrix Regret via Parameter-Free Online Learning
Ashok Cutkosky (Google Research)

强化学习的广义后见
Generalized Hindsight for Reinforcement Learning
Alexander Li (UC Berkeley) · Lerrel Pinto (New York University) · Pieter Abbeel (UC Berkeley & covariant.ai)

学习选择最佳的预测任务以进行临床结果预测
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction
Yuan Xue (Google) · Nan Du (Google Brain) · Anne Mottram (Google Health) · Martin Seneviratne (Google Health) · Andrew Dai (Google Brain)

相关环境下的随机梯度下降:高斯过程的研究
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes
Hao Chen (University of Wisconsin-Madison) · Lili Zheng (University of Wisconsin-Madison) · Raed AL Kontar (University of Michigan) · Garvesh Raskutti (University of Wisconsin-Madison)

学习一些没有条件数界的流行高斯图形模型
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds
Jonathan Kelner (MIT) · Frederic Koehler (MIT) · Raghu Meka (UCLA) · Ankur Moitra (MIT)

X-CAL:生存分析的显式校准
X-CAL: Explicit Calibration for Survival Analysis
Xintian Han (New York University) · Mark Goldstein (New York University) · Aahlad Manas Puli (NYU) · Adler Perotte (Columbia University) · Rajesh Ranganath (New York University)

示范性主动学习
Exemplar Guided Active Learning
Jason Hartford (University of British Columbia) · Kevin Leyton-Brown (University of British Columbia) · Hadas Raviv (AI21 Labs) · Dan Padnos (AI21 Labs) · Shahar Lev (AI21 Labs) · Barak Lenz (AI21 Labs)

平滑的几何图形可实现可靠的归因
Smoothed Geometry for Robust Attribution
Zifan Wang (Carnegie Mellon University) · Haofan Wang (Carnegie Mellon University) · Shakul Ramkumar (Carnegie Mellon University) · Piotr Mardziel (Carnegie Mellon University) · Matt Fredrikson (CMU) · Anupam Datta (Carnegie Mellon University)

吠叫正确的树:搜索分子合成DAG的方法
Barking up the right tree: an approach to search over molecule synthesis DAGs
John Bradshaw (University of Cambridge/MPI IS Tübingen) · Brooks Paige (University College London) · Matt Kusner (University College London) · Marwin Segler (BenevolentAI) · José Miguel Hernández-Lobato (University of Cambridge)

子图神经网络
Subgraph Neural Networks
Emily Alsentzer (MIT) · Samuel Finlayson (Harvard Medical School) · Michelle Li (Harvard Medical School) · Marinka Zitnik (Harvard University)

基于LP的预测和优化的内部点求解
Interior Point Solving for LP-based prediction+optimisation
Jayanta Mandi (Vrije Universiteit Brussel) · Tias Guns (Vrije Universiteit Brussel)

用于半监督学习和局部图聚类的强局部p范切算法
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering
Meng Liu (Purdue University) · David Gleich (Purdue University)

具有3D盒先验的多平面程序归纳
Multi-Plane Program Induction with 3D Box Priors
Yikai Li (Shanghai Jiao Tong University) · Jiayuan Mao (MIT) · Xiuming Zhang (MIT) · Bill Freeman (MIT/Google) · Josh Tenenbaum (MIT) · Noah Snavely (Cornell University and Google AI) · Jiajun Wu (Google)

使用不确定性感知模型识别因果关系推理失败
Identifying Causal Effect Inference Failure with Uncertainty-Aware Models
Andrew Jesson (University of Oxford) · Sören Mindermann (University of Oxford) · Uri Shalit (Technion) · Yarin Gal (University of Oxford)

可变成本结构的顺序贝叶斯实验设计
Sequential Bayesian Experimental Design with Variable Cost Structure
Sue Zheng (MIT) · David Hayden (Massachusetts Institute of Technology) · Jason Pacheco (Univ. of Arizona) · John W Fisher III (MIT)

使用核密度估计的公平分类器
A Fair Classifier Using Kernel Density Estimation
Jaewoong Cho (KAIST) · Gyeongjo Hwang (KAIST) · Changho Suh (KAIST)

关于了解生成对抗网络的全球格局
On Understanding the Global Landscape of Generative Adversarial Nets
Ruoyu Sun (University of Illinois at Urbana-Champaign) · Tiantian Fang (University of Illinois Urbana-Champaign) · Alexander Schwing (University of Illinois at Urbana-Champaign)

稀疏性,共轭性和均值场变分推断
Sparsity, Conjugacy, and Mean Field Variational Inference
Jeffrey Spence (Stanford University)

分布鲁棒优化的大规模方法
Large-Scale Methods for Distributionally Robust Optimization
Daniel Levy (Stanford University) · Yair Carmon (Stanford University) · John Duchi (Stanford) · Aaron Sidford (Stanford)

Flajolet-Martin草绘本身保留了差异性的隐私:最小空间的私人计数
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
Adam Smith (Boston University) · Shuang Song (Google) · Abhradeep Thakurta (Google)

IVs因果效应估计的广义控制函数
Generalized Control Functions for Causal Effect Estimation from IVs
Aahlad Manas Puli (NYU) · Rajesh Ranganath (New York University)

双重Q学习的有限时间分析
Finite-Time Analysis for Double Q-learning
Huaqing Xiong (Ohio State University) · Lin Zhao (National University of Singapore) · Yingbin Liang (The Ohio State University) · Wei Zhang (Southern University of Science and Technology)

理解私人SGD中的梯度裁剪:一个几何视角
Understanding Gradient Clipping in Private SGD: A Geometric Perspective
Xiangyi Chen (University of Minnesota) · Steven Wu (Carnegie Mellon University) · Mingyi Hong (University of Minnesota)

学习战略性网络新兴游戏
Learning Strategic Network Emergence Games
Rakshit Trivedi (Georgia Institute of Technology) · Hongyuan Zha (Georgia Tech)

Multi-ON:使用多对象导航对语义地图内存进行基准测试
Multi-ON: Benchmarking Semantic Map Memory using Multi-Object Navigation
Saim Wani (Indian Institute of Technology Kanpur) · Shivansh Patel (Indian Institute of Technology Kanpur) · Unnat Jain (University of Illinois at Urbana Champaign) · Angel Chang (Simon Fraser University) · Manolis Savva (Simon Fraser University)

NeuralMeshFlow:通过拟形流生成3D流形网格
NeuralMeshFlow: 3D Manifold Mesh Generation via Diffeomorphic Flows
Kunal Gupta (University of California San Diego) · Manmohan Chandraker (UC San Diego)

基于分组的Rank-1格拟蒙特卡罗
Subgroup-based Rank-1 Lattice Quasi-Monte Carlo
Yueming LYU (University of Technology Sydney) · Yuan Yuan (MIT) · Ivor Tsang (University of Technology, Sydney)

稀疏体重激活训练
Sparse Weight Activation Training
Md Aamir Raihan (University of British Columbia) · Tor Aamodt (University of British Columbia)

通过随机平滑验证置信度
Certifying Confidence via Randomized Smoothing
Aounon Kumar (University of Maryland, College Park) · Alexander Levine (University of Maryland, College Park) · Soheil Feizi (University of Maryland) · Tom Goldstein (University of Maryland)

语用沟通的程序综合
Program Synthesis with Pragmatic Communication
Yewen Pu (MIT) · Kevin Ellis (MIT) · Marta Kryven (Massachusetts Institute of Technology) · Josh Tenenbaum (MIT) · Armando Solar-Lezama (MIT)

重尾奖励随机多武装土匪的优化算法
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards
Kyungjae Lee (Seoul National University) · Hongjun Yang (Ulsan National Institute of Science and Technology) · Sungbin Lim (Korea University) · Songhwai Oh (Seoul National University)

通过强大的低等级表示法进行对抗
Adversarial robustness via robust low rank representations
Pranjal Awasthi (Rutgers University/Google) · Himanshu Jain (Google) · Ankit Singh Rawat (Google Research) · Aravindan Vijayaraghavan (Northwestern University)

双歧管对抗鲁棒性:防御Lp和非Lp对抗攻击
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks
Wei-An Lin (Adobe) · Chun Pong Lau (University of Maryland, College Park) · Alexander Levine (University of Maryland, College Park) · Rama Chellappa (University of Maryland College Park) · Soheil Feizi (University of Maryland)

在线发现目标的元梯度强化学习
Meta-Gradient Reinforcement Learning with an Objective Discovered Online
Zhongwen Xu (DeepMind) · Hado van Hasselt (DeepMind) · Matteo Hessel (Google DeepMind) · Junhyuk Oh (DeepMind) · Satinder Singh (DeepMind) · David Silver (DeepMind)

TorsionNet:序列学习者搜索的强化学习方法
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search
Tarun Gogineni (University of Michigan) · Ziping Xu (University of Michigan) · Exequiel Punzalan (University of Michigan) · Runxuan Jiang (University of Michigan) · Joshua Kammeraad (University of Michigan) · Ambuj Tewari (University of Michigan) · Paul Zimmerman (University of Michigan)

公平层次聚类
Fair Hierarchical Clustering
Sara Ahmadian (Google Research) · Alessandro Epasto (Google) · Marina Knittel (University of Maryland, College Park) · Ravi Kumar (Google) · Mohammad Mahdian (Google Research) · Benjamin Moseley (Carnegie Mellon University) · Philip Pham (Google) · Sergei Vassilvitskii (Google) · Yuyan Wang (Carnegie Mellon University)

抓取建议网络:机器人抓取视觉学习的端到端解决方案
Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps
Chaozheng Wu (South China University of Technology) · Jian Chen (South China University of Technology) · Qiaoyu Cao (South China University of Technology ) · Jianchi Zhang (SCUT) · Yunxin Tai (SCUT) · Lin Sun (Samsung, Stanford, HKUST) · Kui Jia (South China University of Technology)

异构数据的分布式训练:桥接基于中值和均值的算法
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms
Xiangyi Chen (University of Minnesota) · Tiancong Chen (University of Minnesota) · Haoran Sun (University of Minnesota) · Steven Wu (Carnegie Mellon University) · Mingyi Hong (University of Minnesota)

稀疏和持续注意机制
Sparse and Continuous Attention Mechanisms
André Martins () · Marcos Treviso (Instituto de Telecomunicacoes) · António Farinhas (Instituto Superior Técnico) · Vlad Niculae (Instituto de Telecomunicações) · Mario Figueiredo (University of Lisbon) · Pedro Aguiar (Instituto Superior Técnico)

表征学习的可分解信息瓶颈
Decodable Information Bottleneck for Representation Learning
Yann Dubois (Facebook AI Research) · Douwe Kiela (Facebook AI Research) · David Schwab (Facebook AI Research) · Ramakrishna Vedantam (Facebook AI Research)

PyGlove:用于自动机器学习的符号编程
PyGlove: Symbolic Programming for Automated Machine Learning
Daiyi Peng (Google) · Xuanyi Dong (University of Technology Sydney) · Esteban Real (Google Brain) · Mingxing Tan (Google Brain) · Yifeng Lu () · Gabriel Bender (Google Brain) · Hanxiao Liu (Google Brain) · Adam Kraft (Google) · Chen Liang (Google Brain) · Quoc V Le (Google)

图神经网络中的超越同质性:当前的局限性和有效的设计
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
Jiong Zhu (University of Michigan) · Yujun Yan (University of Michigan) · Lingxiao Zhao (Carnegie Mellon University) · Mark Heimann (University of Michigan) · Leman Akoglu (CMU) · Danai Koutra (U Michigan)

通过最小化排名范围的总和来学习
Learning by Minimizing the Sum of Ranked Range
Shu Hu (University at Buffalo, State University of New York) · Yiming Ying (State University of New York at Albany) · xin wang (CuraCloud) · Siwei Lyu (University at Albany)

从玻尔兹曼机到神经网络,然后再返回
From Boltzmann Machines to Neural Networks and Back Again
Surbhi Goel (The University of Texas at Austin) · Adam Klivans (UT Austin) · Frederic Koehler (MIT)

BayReL:用于多组学数据集成的贝叶斯关系学习
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
Ehsan Hajiramezanali (Texas A&M University) · Arman Hasanzadeh (Texas A&M University) · Nick Duffield (Texas A&M University) · Krishna Narayanan (Texas A&M University) · Xiaoning Qian (Texas A&M)

MetaPoison:实用的通用清洁标签数据中毒
MetaPoison: Practical General-purpose Clean-label Data Poisoning
W. Ronny Huang (EY) · Jonas Geiping (University of Siegen) · Liam Fowl (University of Maryland) · Gavin Taylor (US Naval Academy) · Tom Goldstein (University of Maryland)

学习熟练的重置:通过无重置游戏获取行为
Learning Skillful Resets: Acquisition of Behavior via Reset-Free Games
Kelvin Xu (UC Berkeley) · Siddharth Verma (UC Berkeley) · Chelsea Finn (Stanford) · Sergey Levine (UC Berkeley)

稀疏的集成神经网络
Sparse Symplectically Integrated Neural Networks
Daniel DiPietro (Dartmouth College) · Shiying Xiong (Dartmouth College) · Bo Zhu (Dartmouth College)

离散图形模型的高效学习
Efficient Learning of Discrete Graphical Models
Marc Vuffray (Los Alamos National Laboratory) · Sidhant Misra (Los Alamos National Laboratory) · Andrey Lokhov (LANL)

自适应梯度法在重尾噪声下收敛
Adaptive Gradient Methods Converge Under Heavy-tailed Noise
Jingzhao Zhang (MIT) · Sai Praneeth Karimireddy (EPFL) · Andreas Veit (Google) · Seungyeon Kim (Google Research) · Sashank Reddi (Google) · Sanjiv Kumar (Google Research) · Suvrit Sra (MIT)

分布稳健的联合平均
Distributionally Robust Federated Averaging
Yuyang Deng (Penn State) · Mohammad Mahdi Kamani (Pennsylvania State University) · Mehrdad Mahdavi (Pennsylvania State University)

密集视觉表示的无监督学习
Unsupervised Learning of Dense Visual Representations
Pedro O. Pinheiro (Element AI) · Amjad Almahairi (Element AI) · Ryan Benmalek (Cornell University) · Florian Golemo (MILA / ElementAI) · Aaron Courville (U. Montreal)

Patch2Self:具有自我监督学习功能的降噪扩散MRI
Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning​
Shreyas Fadnavis (Indiana University Bloomington) · Joshua Batson (CZ Biohub) · Eleftherios Garyfallidis (Indiana University)

高斯过程超参数的与任务无关的摊销推断
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters
Sulin Liu (Princeton) · Xingyuan Sun (Princeton University) · Peter J Ramadge (Princeton) · Ryan Adams (Princeton University)

扎实的语言理解中系统概括的基准
A Benchmark for Systematic Generalization in Grounded Language Understanding
Laura Ruis (University of Amsterdam) · Jacob Andreas (MIT) · Marco Baroni (Facebook Artificial Intelligence Research) · Diane Bouchacourt (Facebook AI) · Brenden Lake (New York University)

深度线性分类中的隐式偏差:初始化量表与训练精度
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy
Edward Moroshko (Technion) · Suriya Gunasekar (Microsoft Research Redmond) · Blake Woodworth (TTIC) · Jason Lee (Princeton University) · Nati Srebro (TTI-Chicago) · Daniel Soudry (Technion)

在连续或大型离散行动空间中进行规划的边际工具
Marginal Utility for Planning in Continuous or Large Discrete Action Spaces
Zaheen F Ahmad (University of Alberta) · Levi Lelis (Universidade Federal de Viçosa) · Michael Bowling (University of Alberta / DeepMind)

用于慢特征分析的生物似然神经网络
A Biologically Plausible Neural Network for Slow Feature Analysis
David Lipshutz (Flatiron Institute) · Charles Windolf (Flatiron Institute) · Siavash Golkar (Flatiron Institute) · Dmitri Chklovskii (Flatiron Institute, Simons Foundation)

对抗式加法攻击的博弈论分析
A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses
Ambar Pal (Johns Hopkins University) · Rene Vidal (Johns Hopkins University, USA)

从最佳决策中学习线性程序
Learning Linear Programs from Optimal Decisions
Yingcong Tan (Concordia University) · Daria Terekhov (Concordia University) · Andrew Delong (Concordia University)

AdvFlow:使用归一化流程进行不起眼的黑匣子对抗攻击
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows
Hadi Mohaghegh Dolatabadi (University of Melbourne) · Sarah Erfani (University of Melbourne) · Christopher Leckie (University of Melbourne)

自适应图卷积递归网络的交通预测
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
LEI BAI (UNSW, Sydney) · Lina Yao (University of New South Wales) · Can Li (University of New South Wales) · Xianzhi Wang (University of Technology Sydney) · Can Wang (Griffith University)

具有Bandit反馈的非随机控制
Non-Stochastic Control with Bandit Feedback
Paula Gradu (Princeton University, Google AI Princeton) · John Hallman (Princeton University) · Elad Hazan (Princeton University)

辅助学习:多个组织学习的框架
Assisted Learning: A Framework for Multiple Organizations Learning
Xun Xian (University of Minnesota) · Xinran Wang (University of Minnesota) · Jie Ding (University of Minnesota) · Reza Ghanadan (Google)

图数据的不确定性感知半监督学习
Uncertainty Aware Semi-Supervised Learning on Graph Data
Xujiang Zhao (The University of Texas at Dallas) · Feng Chen (UT Dallas) · Shu Hu (University at Buffalo, State University of New York) · Jin-Hee Cho (Virginia Tech)

具有O(1 / ϵ2)迭代复杂度的非凸凹极小极大问题的平滑GDA算法
A smoothed GDA algorithm for the nonconvex-concave min-max problem with an O(1/ϵ2) iteration complexity
Jiawei Zhang (The Chinese University of Hong Kong, Shenzhen) · Peijun Xiao (University of Illinois at Urbana-Champaign (UIUC)) · Ruoyu Sun (University of Illinois at Urbana-Champaign) · Zhiquan Luo (The Chinese University of Hong Kong, Shenzhen and Shenzhen Research Institute of Big Data)

通过神经元对齐优化模式连接
Optimizing Mode Connectivity via Neuron Alignment
Norman Tatro (Rensselaer Polytechnic Institute) · Pin-Yu Chen (IBM Research AI) · Payel Das (IBM Research) · Igor Melnyk (IBM Research) · Prasanna Sattigeri (IBM Research) · Rongjie Lai (Rensselaer Polytechnic Institute)

通过ℓ1正则化转移学习
Transfer Learning via ℓ1 Regularization
Masaaki Takada (Toshiba Corporation) · Hironori Fujisawa (The Institute of Statistical Mathematics)

无所畏惧的DAG:仔细研究学习贝叶斯网络的持续优化
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
Dennis Wei (IBM Research) · Tian Gao (IBM Research AI) · yue yu (Lehigh University)

自动同步:学习同步以实现数据并行分布式深度学习
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning
Hao Zhang (Carnegie Mellon University, Petuum Inc.) · Yuan Li (Duke University) · Zhijie Deng (Tsinghua University) · Xiaodan Liang (Sun Yat-sen University) · Lawrence Carin (Duke University) · Eric Xing (Petuum Inc. / Carnegie Mellon University)

输入感知的动态后门攻击
Input-Aware Dynamic Backdoor Attack
Tuan Anh Nguyen (VinAI Research/Hanoi University of Science and Technology) · Anh Tran (VinAI Research)

部分按视图对齐的聚类
Partially View-aligned Clustering
Zhenyu Huang (Sichuan University) · Peng Hu (Institute for Infocomm Research, ASTAR) · Joey Tianyi Zhou (IHPC, ASTAR) · Jiancheng Lv (Machine Intelligence Laboratory College of Computer Science, Sichuan University) · Xi Peng (Institute for Infocomm, Research Agency for Science, Technology and Research (A*STAR) Singapore)

韦斯顿·沃特金斯合页损失和有序分区
Weston-Watkins Hinge Loss and Ordered Partitions
Yutong Wang (University of Michigan) · Clayton Scott (University of Michigan)

具有临时消息控制的简洁可靠的多代理通信
Succinct and Robust Multi-Agent Communication With Temporal Message Control
Sai Qian Zhang (Harvard University) · Qi Zhang (Amazon) · Jieyu Lin (University of Toronto)

在非真实拍卖中学习实用工具和均衡
Learning Utilities and Equilibria in Non-Truthful Auctions
Hu Fu (University of British Columbia) · Tao Lin (Peking University)

使用Moreau信封进行个性化联合学习
Personalized Federated Learning with Moreau Envelopes
Canh T. Dinh (The University of Sydney) · Nguyen H. Tran (The University of Sydney) · Tuan Dung Nguyen (The University of Melbourne)

我该如何向您解释?深度神经网络解释方法的实证研究
How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods
Jeya Vikranth Jeyakumar (University of California, Los Angeles) · Joseph Noor (University of California, Los Angeles) · Yu-Hsi Cheng (UCLA) · Luis Garcia (University of California, Los Angeles) · Mani Srivastava (UCLA)

迈尔森回归
Myersonian Regression
Allen Liu (MIT) · Renato Leme (Google Research) · Jon Schneider (Google Research)

通过深度强化学习来学习调度车间调度
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Cong Zhang (Nanyang Technological University) · Wen Song (Institute of Marine Scinece and Technology, Shandong University) · Zhiguang Cao (National University of Singapore) · Jie Zhang (Nanyang Technological University) · Puay Siew Tan (SIMTECH) · Xu Chi (Singapore Institute of Manufacturing Technology, A-Star)

通过特征函数进行对等对抗学习
Reciprocal Adversarial Learning via Characteristic Functions
Shengxi Li (Imperial College London) · Zeyang Yu (Imperial College London) · Min Xiang (Imperial College London) · Danilo P Mandic (Imperial College London)

奖励-理性(内隐)选择:奖励学习的统一形式
Reward-rational (implicit) choice: A unifying formalism for reward learning
Hong Jun Jeon (Stanford University) · Smitha Milli (UC Berkeley) · Anca Dragan (UC Berkeley)

通过功能梯度统计查询下界
Statistical-Query Lower Bounds via Functional Gradients
Surbhi Goel (The University of Texas at Austin) · Aravind Gollakota (University of Texas at Austin) · Adam Klivans (UT Austin)

抽样可分解的生成对抗性推荐器
Sampling-Decomposable Generative Adversarial Recommender
Binbin Jin (University of Science and Technology of China) · Defu Lian (University of Science and Technology of China) · Zheng Liu (Microsoft) · Qi Liu (“ University of Science and Technology of China, China”) · Jianhui Ma (University of Science and Technology of China) · Xing Xie (Microsoft Research Asia) · Enhong Chen (University of Science and Technology of China)

学习模型的边界厚度和鲁棒性
Boundary thickness and robustness in learning models
Yaoqing Yang (UC Berkeley) · Rajiv Khanna (University of California, Berkeley) · Yaodong Yu (University of California, Berkeley) · Amir Gholami (University of California, Berkeley) · Kurt Keutzer (EECS, UC Berkeley) · Joseph Gonzalez (UC Berkeley) · Kannan Ramchandran (UC Berkeley) · Michael W Mahoney (UC Berkeley)

对抗性鲁棒性的生物学启发机制
Biologically Inspired Mechanisms for Adversarial Robustness
Manish Vuyyuru Reddy (Harvard) · Andrzej Banburski (MIT) · Nishka Pant (MIT) · Tomaso Poggio (MIT)

开关约束在线凸优化的Minimax后悔:无相变
Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition
Lin Chen (University of California, Berkeley) · Qian Yu (University of Southern California) · Hannah Lawrence (Flatiron Institute) · Amin Karbasi (Yale)

学习使用指令指针注意图神经网络执行程序
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
David Bieber (Google Brain) · Charles Sutton (Google) · Hugo Larochelle (Google Brain) · Daniel Tarlow (Google Brain)

以解释为潜在变量实现可解释的自然语言理解
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables
Wangchunshu Zhou (Beihang University) · Jinyi Hu (Tsinghua University) · Hanlin Zhang (South China University of Technology) · Xiaodan Liang (Sun Yat-sen University) · Maosong Sun (Tsinghua University) · Chenyan Xiong (Microsoft Research AI) · Jian Tang (Mila)

高维回归的随机测试:更有效,更强大的解决方案
Randomized tests for high-dimensional regression: A more efficient and powerful solution
Yue Li (Carnegie Mellon University) · Ilmun Kim (CMU) · Yuting Wei (Carnegie Mellon University)

通过双重学习率的不正确学习率的隐式偏差进行稳健恢复
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization
Chong You (University of California, Berkeley) · Zhihui Zhu (Johns Hopkins University) · Qing Qu (New York University) · Yi Ma (UC Berkeley)

对抗性对比学习:从无监督的预训练中获得更多的鲁棒性
Adversarial Contrastive Learning: Harvesting More Robustness from Unsupervised Pre-Training
Ziyu Jiang (Texas A&M University) · Tianlong Chen (Unversity of Texas at Austin) · Ting Chen (Google) · Zhangyang Wang (University of Texas at Austin)

插件求解器对基于特征的强化学习有效吗?
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?
Qiwen Cui (Peking University) · Lin Yang (UCLA)

Erdos-Renyi随机网络的在线凸优化
Online Convex Optimization Over Erdos-Renyi Random Networks
Jinlong Lei (Tongji University) · Peng Yi (Tongji University) · Yiguang Hong (Academy of Mathematics and Systems Science, Chinese Academy of Sciences) · Jie Chen (Beijing Institute of Technology) · Guodong Shi (University of Sydney)

通过学习对结构化高维分布进行有效距离逼近
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning
Arnab Bhattacharyya (National University of Singapore) · Sutanu Gayen (National University of SIngapore) · Kuldeep S Meel (National University of Singapore) · N. V. Vinodchandran (University of Nebraska)

通过顺序决策和ML预测来改进在线租赁算法
Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions
Soumya Banerjee (Minnesota State University, Mankato)

可解释性和个性化的学徒制计划:从异构用户演示中学习可解释性计划策略
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations
Rohan Paleja (Georgia Institute of Technology) · Andrew Silva (Georgia Institute of Technology) · Letian Chen (Georgia Institute of Technology) · Matthew Gombolay (Georgia Institute of Technology)

更好地推广自适应梯度法
Towards Better Generalization of Adaptive Gradient Methods
Yingxue Zhou (University of Minnesota) · Belhal Karimi (Baidu Research) · Jinxing Yu (Baidu Research) · Zhiqiang Xu (Baidu Inc.) · Ping Li (Baidu Research USA)

渐进最优精确小批量都市圈
Asymptotically Optimal Exact Minibatch Metropolis-Hastings
Ruqi Zhang (Cornell University) · A. Feder Cooper (Cornell University) · Christopher De Sa (Cornell)

线性宽度神经网络的共轭核和神经正切核的光谱
Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks
Zhou Fan (Yale University) · Zhichao Wang (UC San Diego)

强化学习中基于实例的概括
Instance-based Generalization in Reinforcement Learning
Martin Bertran (Duke University) · Natalia L Martinez (Duke University) · Mariano Phielipp (Intel AI Labs) · Guillermo Sapiro (Duke University)

使用快速低基数半定编程进行社区检测
Community detection using fast low-cardinality semidefinite programming
Po-Wei Wang (CMU) · J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

公平绩效指标征集
Fair Performance Metric Elicitation
Gaurush Hiranandani (University of Illinois at Urbana-Champaign) · Harikrishna Narasimhan (Google Research) · Oluwasanmi Koyejo (UIUC)

具有递归生成反馈的神经网络
Neural Networks with Recurrent Generative Feedback
Yujia Huang (Caltech) · James Gornet (California Institute of Technology) · Sihui Dai (California Institute of Technology) · Zhiding Yu (NVIDIA) · Tan Nguyen (Rice University/UCLA) · Doris Tsao (Caltech) · Anima Anandkumar (NVIDIA / Caltech)

确定性梯度下降的随机性:多尺度目标函数的大学习率
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function
Lingkai Kong (Georgia Institute of Technology) · Molei Tao (Georgia Institute of Technology)

使用最大平均差异校准的可靠回归
Calibrated Reliable Regression using Maximum Mean Discrepancy
Peng Cui (Tsinghua University) · Wenbo Hu (Tsinghua University) · Jun Zhu (Tsinghua University)

对抗性线性强盗的紧紧的一阶和二阶后悔范围
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits
Shinji Ito (NEC Corporation) · Shuichi Hirahara (National Institute of Informatics) · Tasuku Soma (University of Tokyo) · Yuichi Yoshida (National Institute of Informatics and Preferred Infrastructure, Inc.)

深层直接可能性仿制
Deep Direct Likelihood Knockoffs
Mukund Sudarshan (NYU) · Wesley Tansey (Columbia University) · Rajesh Ranganath (New York University)

AViD数据集:来自不同国家的匿名视频
AViD Dataset: Anonymized Videos from Diverse Countries
Anthony J Piergiovanni (Indiana University) · Michael S Ryoo (Stony Brook University)

如何表征超参数化卷积神经网络的格局
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks
Weizhong Zhang (Zhejiang University) · Yihong Gu (Princeton University) · Cong Fang (Peking University) · Jason Lee (Princeton University) · Tong Zhang (Tencent AI Lab)

针对DNN的实用无框对抗攻击
Practical No-box Adversarial Attacks against DNNs
Qizhang Li (ByteDance AI Lab) · Yiwen Guo (ByteDance AI Lab) · Hao Chen (UC Davis)

有限时间保证的基于偏好的强化学习
Preference-based Reinforcement Learning with Finite-Time Guarantees
Yichong Xu (Carnegie Mellon University) · Ruosong Wang (Carnegie Mellon University) · Lin Yang (UCLA) · Aarti Singh (CMU) · Artur Dubrawski (Carnegie Mellon University)

平滑正则分类器的证明稳健性的一致性正则化
Consistency Regularization for Certified Robustness of Smoothed Classifiers
Jongheon Jeong (KAIST) · Jinwoo Shin (KAIST)

通过神经程序综合学习组成规则
Learning Compositional Rules via Neural Program Synthesis
Maxwell Nye (MIT) · Armando Solar-Lezama (MIT) · Josh Tenenbaum (MIT) · Brenden Lake (New York University)

知识图中多跳逻辑推理的Beta嵌入
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
Hongyu Ren (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

深度生成网络的分析概率分布和EM学习
Analytical Probability Distributions and EM-Learning for Deep Generative Networks
Randall Balestriero (Rice University) · Sebastien PARIS (University of Toulon) · Richard Baraniuk (Rice University)

无需培训即可预测培训时间
Predicting Training Time Without Training
Luca Zancato (University of Padova) · Alessandro Achille (Amazon Web Services) · Avinash Ravichandran (AWS) · Rahul Bhotika (Amazon) · Stefano Soatto (UCLA)

分段保守线性强盗
Stage-wise Conservative Linear Bandits
Ahmadreza Moradipari (University of California, Santa Barbara) · Christos Thrampoulidis (UCSB) · Mahnoosh Alizadeh (University of California Santa Barbara)

学习解码:增强学习,用于基于稀疏图的通道代码的解码
Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes
Salman Habib (New Jersey Institute of Tech) · Allison Beemer (New Jersey Institute of Technology) · Joerg Kliewer (New Jersey Institute of Technology)

拓扑机器学习的多参数持久性图像
Multiparameter Persistence Image for Topological Machine Learning
Mathieu Carrière (Inria Sophia Antipolis) · Andrew Blumberg (University of Texas)

紧下界和有效减少掉期后悔
A Tight Lower Bound and Efficient Reduction for Swap Regret
Shinji Ito (NEC Corporation)

具有理论保证的个性化联合学习:与模型无关的元学习方法
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
Alireza Fallah (MIT) · Aryan Mokhtari (UT Austin) · Asuman Ozdaglar (Massachusetts Institute of Technology)

什么形状的特征表示?探索数据集,架构和培训
What shapes feature representations? Exploring datasets, architectures, and training
Katherine Hermann (Stanford University) · Andrew Lampinen (Stanford University)

权重标准化的隐式正则化和收敛性
Implicit Regularization and Convergence for Weight Normalization
Xiaoxia Wu (The University of Texas at Austin) · Edgar Dobriban (University of Pennsylvania) · Tongzheng Ren (UT Austin) · Shanshan Wu (University of Texas at Austin) · Zhiyuan Li (Princeton University) · Suriya Gunasekar (Microsoft Research Redmond) · Rachel Ward (UT Austin) · Qiang Liu (Dartmouth College)

视频中物理系统的因果发现
Causal Discovery in Physical Systems from Videos
Yunzhu Li (MIT) · Antonio Torralba (Massachusetts Institute of Technology) · Anima Anandkumar (NVIDIA / Caltech) · Dieter Fox (NVIDIA) · Animesh Garg (Univ. of Toronto, Vector Institute, Nvidia)

扰乱功能层次结构以提高标准和严格的黑盒攻击传递能力
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
Nathan Inkawhich (Duke University) · Kevin Liang (Duke University) · Binghui Wang (Duke University) · Matthew J Inkawhich (Duke University) · Lawrence Carin (Duke University) · Yiran Chen (Duke University)

从神经网络可观察物识别学习规则
Identifying learning rules from neural network observables
Aran Nayebi (Stanford University) · Sanjana Srivastava (Stanford University) · Surya Ganguli (Stanford) · Daniel Yamins (Stanford University)

了解和探索具有随机体系结构的网络
Understanding and Exploring the Network with Stochastic Architectures
Zhijie Deng (Tsinghua University) · Yinpeng Dong (Tsinghua University) · Shifeng Zhang (Department of Computer Science and Technology, Tsinghua University) · Jun Zhu (Tsinghua University)

多标签对比预测编码
Multi-label Contrastive Predictive Coding
Jiaming Song (Stanford University) · Stefano Ermon (Stanford)

梯度电磁贝叶斯元学习
Gradient-EM Bayesian Meta-Learning
Yayi Zou (Didi Research America) · Xiaoqi Lu (Columbia University)

BAIL:用于批量深度强化学习的最佳动作模仿学习
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
Xinyue Chen (NYU Shanghai) · Zijian Zhou (NYU Shanghai) · Zheng Wang (NYU Shanghai) · Che Wang (New York University) · Yanqiu Wu (New York University) · Keith Ross (NYU Shanghai)

错误指定的分类:半空间,广义线性模型和可演化性
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability
Sitan Chen (MIT) · Frederic Koehler (MIT) · Ankur Moitra (MIT) · Morris Yau (UC Berkeley)

无限混合高斯过程的与任务无关的在线强化学习
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
Mengdi Xu (Carnegie Mellon University) · Wenhao Ding (Carnegie Mellon University) · Jiacheng Zhu (Carnegie Mellon University) · ZUXIN LIU (Carnegie Mellon University) · Baiming Chen (Tsinghua University) · Ding Zhao (Carnegie Mellon University)

分布式牛顿可以减少沟通并抵抗拜占庭工人
Distributed Newton Can Communicate Less and Resist Byzantine Workers
Avishek Ghosh (University of California, Berkeley) · Raj Kumar Maity (University of Massachusetts Amherst) · Arya Mazumdar (University of Massachusetts Amherst)

分散的GCN:克服图卷积网络中的过度平滑
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks
Yimeng Min (MILA) · Frederik Wenkel (Mila, Université de Montréal) · Guy Wolf (Université de Motréal; Mila)

一目了然:减少图像分类中空间冗余的动态方法
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
Yulin Wang (Tsinghua University) · Kangchen Lv (Tsinghua University) · Rui Huang (Tsinghua University) · Shiji Song (Department of Automation, Tsinghua University) · Le Yang (Tsinghua University) · Gao Huang (Tsinghua)

重新检查线性嵌入以进行高维贝叶斯优化
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
Ben Letham (Facebook) · Roberto Calandra (Facebook AI Research) · Akshara Rai (Facebook) · Eytan Bakshy (Facebook)

从不受信任的批次中学习结构化发行:更快,更简单
Learning Structured Distributions From Untrusted Batches: Faster and Simpler
Sitan Chen (MIT) · Jerry Li (Microsoft) · Ankur Moitra (MIT)

关于对抗性示例防御的自适应攻击
On Adaptive Attacks to Adversarial Example Defenses
Florian Tramer (Stanford University) · Nicholas Carlini (Google) · Wieland Brendel (University of Tübingen) · Aleksander Madry (MIT)

通过高阶分割准则为决策树归纳的通用保证
Universal guarantees for decision tree induction via a higher-order splitting criterion
Guy Blanc (Stanford University) · Neha Gupta (Stanford University) · Jane Lange (Stanford University) · Li-Yang Tan (Stanford University)

OTLDA:用于主题建模的几何感知最佳传输方法
OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling
Viet Huynh (Monash University) · He Zhao (Monash University) · Dinh Phung (Monash University)

具有生成先验的低秩矩阵恢复的非渐近保证
Nonasymptotic Guarantees for Low-Rank Matrix Recovery with Generative Priors
Jorio Cocola (Northeastern University) · Paul Hand (Northeastern University) · Vlad Voroninski (Helm.ai)

Funnel-Transformer:过滤出顺序冗余以进行有效的语言处理
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
Zihang Dai (Carnegie Mellon University) · Guokun Lai (Carnegie Mellon University) · Yiming Yang (CMU) · Quoc V Le (Google)

具有线性函数逼近的无奖励强化学习
On Reward-Free Reinforcement Learning with Linear Function Approximation
Ruosong Wang (Carnegie Mellon University) · Simon Du (Institute for Advanced Study) · Lin Yang (UCLA) · Russ Salakhutdinov (Carnegie Mellon University)

神经网络的广义杠杆得分抽样
Generalized Leverage Score Sampling for Neural Networks
zheng Yu (Princeton University) · Ruoqi Shen (University of Washington) · Zhao Song (IAS/Princeton) · Jason Lee (Princeton University) · Mengdi Wang (Princeton University)

功能混杂因素的因果估计
Causal Estimation with Functional Confounders
Aahlad Manas Puli (NYU) · Adler Perotte (Columbia University) · Rajesh Ranganath (New York University)

使用多对数样本复杂度估计决策树可学习性
Estimating decision tree learnability with polylogarithmic sample complexity
Guy Blanc (Stanford University) · Neha Gupta (Stanford University) · Jane Lange (Stanford University) · Li-Yang Tan (Stanford University)

AvE:通过授权提供协助
AvE: Assistance via Empowerment
Yuqing Du (UC Berkeley) · Stas Tiomkin (EECS Department, University of California, Berkeley) · Emre Kiciman (Microsoft Research) · Daniel Polani (University of Hertfordshire) · Pieter Abbeel (UC Berkeley & covariant.ai) · Anca Dragan (UC Berkeley)

一阶后悔边界在线次模最大化的改进算法
Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds
Nicholas Harvey (University of British Columbia) · Christopher Liaw (University of British Columbia) · Tasuku Soma (University of Tokyo)

小组知识转移:大型CNN的协作培训
Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge
Chaoyang He (University of Southern California) · Murali Annavaram (University of Southern California) · Salman Avestimehr (University of Southern California)

张量完成实用
Tensor Completion Made Practical
Allen Liu (MIT) · Ankur Moitra (MIT)

元学习需要元增强
Meta-Learning Requires Meta-Augmentation
Janarthanan Rajendran (University of Michigan) · Alexander Irpan (Google Brain) · Eric Jang (Google Brain)

具有许多提示的在线线性优化
Online Linear Optimization with Many Hints
Aditya Bhaskara (University of Utah) · Ashok Cutkosky (Google Research) · Ravi Kumar (Google) · Manish Purohit (Google)

间接监控信号的易学性
Learnability with Indirect Supervision Signals
Kaifu Wang (University of Pennsylvania) · Qiang Ning (Allen Institute for AI) · Dan Roth (UPenn)

贝叶斯关于训练速度和模型选择的观点
A Bayesian Perspective on Training Speed and Model Selection
Clare Lyle (University of Oxford) · Lisa Schut (University of Oxford) · Robin Ru (Oxford University) · Yarin Gal (University of Oxford) · Mark van der Wilk (Imperial College)

无监督的信息理论感知质量指标
An Unsupervised Information-Theoretic Perceptual Quality Metric
Sangnie Bhardwaj (Google LLC) · Ian Fischer (Google) · Johannes Ballé (Google) · Troy Chinen (Google)

霍克斯网络推断的不确定度量化
Uncertainty Quantification for Inferring Hawkes Networks
Haoyun Wang (Georgia Tech) · Liyan Xie (Georgia Institute of Technology) · Alex Cuozzo (Duke University) · Simon Mak (Duke University) · Yao Xie (Georgia Institute of Technology)

贝叶斯注意模块
Bayesian Attention Modules
Xinjie Fan (UT Austin) · Shujian Zhang (UT Austin) · Bo Chen (Xidian University) · Mingyuan Zhou (University of Texas at Austin)

我可以相信我的公平指标吗?使用未标记数据和贝叶斯推断评估公平性
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
Disi Ji (UC, Irvine) · Padhraic Smyth (University of California, Irvine) · Mark Steyvers (UC Irvine)

两层神经网络的动态中心极限定理
A Dynamical Central Limit Theorem for Two-Layer Neural Networks
Zhengdao Chen (New York University) · Grant Rotskoff (New York University) · Joan Bruna (NYU) · Eric Vanden-Eijnden (New York University)

解决异构联合优化中的目标不一致问题
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
Jianyu Wang (Carnegie Mellon University) · Qinghua Liu (Princeton University) · Hao Liang (Carnegie Mellon University) · Gauri Joshi (Carnegie Mellon University) · H. Vincent Poor (Princeton University)

自我游戏的近乎最佳强化学习
Near-Optimal Reinforcement Learning with Self-Play
Yu Bai (Salesforce Research) · Chi Jin (Princeton University) · Tiancheng Yu (MIT )

通过代理随机设计进行双下降和隐式正则化的精确表达式
Exact expressions for double descent and implicit regularization via surrogate random design
Michal Derezinski (UC Berkeley) · Feynman T Liang (Berkeley) · Michael W Mahoney (UC Berkeley)

高/宽线性程序的更快的随机不可行内点方法
Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs
Agniva Chowdhury (Purdue University) · Palma London (Caltech) · Haim Avron (Tel Aviv University) · Petros Drineas (Purdue University)

通过子集总和获得最佳彩票:对数过度参数化足够
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient
Ankit Pensia (University of Wisconsin-Madison) · Shashank Rajput (University of Wisconsin - Madison) · Alliot Nagle (UW-Madison) · Harit Vishwakarma (University of Wisconsin Madison) · Dimitris Papailiopoulos (University of Wisconsin-Madison)

最大化受过滤约束的福利和收入:一种算法观点
Maximizing Welfare and Revenue Subject to a Filtering Constraint: An Algorithmic Viewpoint
Aranyak Mehta (Google Research) · Uri Nadav (Google) · Alexandros Psomas (Purdue University) · Aviad Rubinstein (Stanford)

使用有效空间变化核的全卷积网格自动编码器
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels
Yi Zhou (University of Southern California) · Chenglei Wu (Facebook) · Zimo Li (University of Southern California) · Chen Cao (Snap Inc.) · Yuting Ye (Facebook Reality Labs) · Jason Saragih (Facebook) · Hao Li (Pinscreen/University of Southern California/USC ICT) · Yaser Sheikh (Facebook Reality Labs)

捆绑治疗的反事实预测
Counterfactual Prediction for Bundle Treatment
Hao Zou (Tsinghua University) · Peng Cui (Tsinghua University) · Bo Li (Tsinghua University) · Zheyan Shen (Tsinghua University) · Jianxin Ma (Alibaba Group) · Hongxia Yang (Alibaba Group) · Yue He (Tsinghua University)

重新思考标签的价值,以改善班级不平衡的学习
Rethinking the Value of Labels for Improving Class-Imbalanced Learning
Yuzhe Yang (MIT) · Zhi Xu (MIT)

辍学培训的收敛性与推广性
On Convergence and Generalization of Dropout Training
Poorya Mianjy (Johns Hopkins University) · Raman Arora (Johns Hopkins University)

在随机土匪中找到所有ϵ良好武器
Finding All ϵ-Good Arms in Stochastic Bandits
Blake Mason (University of Wisconsin - Madison) · Lalit Jain (University of Washington) · Ardhendu Tripathy (University of Wisconsin - Madison) · Robert Nowak (University of Wisconsion-Madison)

FrugalML:如何更准确,更便宜地使用ML Prediction API
FrugalML: How to use ML Prediction APIs more accurately and cheaply
Lingjiao Chen (University of Wisconsin-Madison) · Matei Zaharia (Stanford and Databricks) · James Zou (Stanford University)

努力理解分层学习:神经表示的好处
Towards Understanding Hierarchical Learning: Benefits of Neural Representations
Minshuo Chen (Georgia Tech) · Yu Bai (Salesforce Research) · Jason Lee (Princeton University) · Tuo Zhao (Gatech) · Huan Wang (Salesforce Research) · Caiming Xiong (Salesforce) · Richard Socher (Salesforce)

从非线性观测中学习线性二次调节器
Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi (The Australian National University and Data61) · Dylan Foster (MIT) · Max Simchowitz (Berkeley) · Wen Sun (Microsoft Research NYC) · Dipendra Misra (Microsoft) · Akshay Krishnamurthy (Microsoft) · Alexander Rakhlin (MIT) · John Langford (Microsoft Research New York)

Wasserstein判别分析的比率迹线公式
Ratio Trace Formulation of Wasserstein Discriminant Analysis
Hexuan Liu (University of Washington) · Yunfeng Cai (Baidu Research) · You-Lin Chen (Department of Statistics, University of Chicago) · Ping Li (Baidu Research USA)

差分私有聚类:紧逼近比
Differentially Private Clustering: Tight Approximation Ratios
Badih Ghazi (Google) · Ravi Kumar (Google) · Pasin Manurangsi (Google)

解决硬AI计划实例的新颖的自动课程策略
A Novel Automated Curriculum Strategy to Solve Hard AI Planning Instances
Dieqiao Feng (Cornell University) · Carla Gomes (Cornell University) · Bart Selman (Cornell University)

具有模型不确定性的鲁棒多智能体强化学习
Robust Multi-Agent Reinforcement Learning with Model Uncertainty
Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · TAO SUN (Amazon.com) · Yunzhe Tao (Amazon Artificial Intelligence) · Sahika Genc (Amazon Artificial Intelligence) · Sunil Mallya (Amazon AWS) · Tamer Basar (University of Illinois at Urbana-Champaign)

从观察法到模拟到真实转移的模仿
An Imitation from Observation Approach to Sim-to-Real Transfer
Siddharth Desai (University of Texas at Austin) · Ishan Durugkar (University of Texas at Austin) · Haresh Karnan (University of Texas at Austin) · Garrett Warnell (US Army Research Laboratory) · Josiah Hanna ( University of Edinburgh) · Peter Stone (The University of Texas at Austin)

列子集选择和Nystrom方法的改进保证和多重下降曲线
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method
Michal Derezinski (UC Berkeley) · Rajiv Khanna (University of California, Berkeley) · Michael W Mahoney (UC Berkeley)

通过显式约束简化哈密顿神经网络和拉格朗日神经网络
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
Marc Finzi (New York University) · Ke Alexander Wang (Cornell University) · Andrew Gordon Wilson (New York University)

使非随机控制(几乎)像随机控制一样容易
Making Non-Stochastic Control (Almost) as Easy as Stochastic
Max Simchowitz (Berkeley)

频谱卡尔曼滤波:学习在具有长期记忆的未知动力系统中进行预测
Spectral Kalman filtering: Learning to predict in unknown dynamical systems with long-term memory
Paria Rashidinejad (University of California, Berkeley) · Jiantao Jiao (University of California, Berkeley) · Stuart Russell (UC Berkeley)

面向因式马尔可夫决策过程的Minimax最优强化学习
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
Yi Tian (MIT) · Jian Qian (MIT) · Suvrit Sra (MIT)

预训练的BERT网络的彩票假设
The Lottery Ticket Hypothesis for the Pre-trained BERT Networks
Tianlong Chen (Unversity of Texas at Austin) · Jonathan Frankle (MIT CSAIL) · Shiyu Chang (MIT-IBM Watson AI Lab) · Sijia Liu (MIT-IBM Watson AI Lab, IBM Research) · Yang Zhang (MIT-IBM Watson AI Lab) · Zhangyang Wang (University of Texas at Austin) · Michael Carbin (MIT)

部分可观测线性动力系统中的对数后悔绑定
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems
Ali Sahin Lale (California Institute of Technology) · Kamyar Azizzadenesheli (Caltech) · Babak Hassibi (Caltech) · Anima Anandkumar (NVIDIA / Caltech)

随机投影的精确表达式:低秩逼近和随机牛顿
Precise expressions for random projections: Low-rank approximation and randomized Newton
Michal Derezinski (UC Berkeley) · Feynman T Liang (Berkeley) · Zhenyu Liao (University of California, Berkeley) · Michael W Mahoney (UC Berkeley)

统计最优输运构成学习核嵌入
Statistical Optimal Transport posed as Learning Kernel Embedding
Saketha Nath Jagarlapudi (IIT Hyderabad) · Pratik Kumar Jawanpuria (Microsoft)

生成模型的Sinkhorn自然梯度
Sinkhorn Natural Gradient for Generative Models
Zebang Shen (University of Pennsylvania) · Zhenfu Wang (Peking University) · Alejandro Ribeiro (University of Pennsylvania) · Hamed Hassani (UPenn)

训练更强的基线以学习优化
Training Stronger Baselines for Learning to Optimize
Tianlong Chen (Unversity of Texas at Austin) · Weiyi Zhang (Shanghai Jiao Tong University) · Zhou Jingyang (University of Science and Technology of China) · Shiyu Chang (MIT-IBM Watson AI Lab) · Sijia Liu (MIT-IBM Watson AI Lab, IBM Research) · Lisa Amini (IBM Research) · Zhangyang Wang (University of Texas at Austin)

使用错误指定的模拟器进行离线模仿学习
Offline Imitation Learning with a Misspecified Simulator
Shengyi Jiang (Nanjing University) · Jingcheng Pang (Nanjing University) · Yang Yu (Nanjing University)

多层模型中的推论和估计
Inference and Estimation in Multi-Layer Models
Parthe Pandit (University of California, Los Angeles) · Mojtaba Sahraee Ardakan (UCLA) · Sundeep Rangan (NYU) · Philip Schniter (The Ohio State University) · Alyson Fletcher (UCLA)

通过功能梯度下降获得Sinkhorn重心
Sinkhorn Barycenter via Functional Gradient Descent
Zebang Shen (University of Pennsylvania) · Zhenfu Wang (Peking University) · Alejandro Ribeiro (University of Pennsylvania) · Hamed Hassani (UPenn)

通过保留突触流修剪没有任何数据的神经网络
Pruning neural networks without any data by conserving synaptic flow
Hidenori Tanaka (NTT Research, PHI Lab / Stanford University) · Daniel Kunin (Stanford University) · Daniel Yamins (Stanford University) · Surya Ganguli (Stanford)

通过一站式多步树进行有效的非近视贝叶斯优化
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Shali Jiang (Washington University in St. Louis) · Daniel Jiang (Facebook) · Maximilian Balandat (Facebook) · Brian Karrer (Facebook) · Jacob Gardner (University of Pennsylvania) · Roman Garnett (Washington University in St. Louis)

使用代理草绘和比例尺正则化对分布式二阶优化进行去偏
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization
Michal Derezinski (UC Berkeley) · Burak Bartan (Stanford University) · Mert Pilanci (Stanford) · Michael W Mahoney (UC Berkeley)

具有平均奖励的网络系统的可扩展多Agent强化学习
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward
Guannan Qu (California Institute of Technology) · Yiheng Lin (California Institute of Technology) · Adam Wierman (California Institute of Technology) · Na Li (Harvard University)

实用的自动化数据扩充功能,减少了搜索空间
Practical automated data augmentation with a reduced search space
Ekin Dogus Cubuk (Google Brain) · Barret Zoph (Google Brain) · Jon Shlens (Google Research) · Quoc V Le (Google)

通过速度缩放学习增强的能量最小化
Learning Augmented Energy Minimization via Speed Scaling
Etienne Bamas (EPFL) · Andreas Maggiori (EPFL) · Lars Rohwedder (EPFL) · Ola Svensson (EPFL)

GCOMB:在十亿张图上学习预算受限的组合算法
GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs
Sahil Manchanda (IIT Delhi) · AKASH MITTAL (IIT Delhi) · Anuj Dhawan (Indian Institute of Technology Delhi) · Sourav Medya (Kellogg School of Management, Northwestern University) · Sayan Ranu (IIT Delhi) · Ambuj K Singh (UNIVERSITY OF CALIFORNIA, SANTA BARBARA)

再谈高斯过程的稀疏谱近似的样本复杂性
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
Minh Hoang (Carnegie Mellon University) · Nghia Hoang (MIT-IBM Watson AI Lab, IBM Research) · Hai Pham (Carnegie Mellon University) · David Woodruff (Carnegie Mellon University)

AMQ:基于Hessian跟踪的自动混合精度量化
AMQ: Automatic Mixed-precision Quantization Based on Hessian Trace
Zhen Dong (UC Berkeley) · Zhewei Yao (UC Berkeley) · Daiyaan Arfeen (UC Berkeley) · Amir Gholami (University of California, Berkeley) · Michael Mahoney (UC Berkeley) · Kurt Keutzer (EECS, UC Berkeley)

持续学习一系列混合任务
Continual Learning of a Sequence of Mixed Tasks
Zixuan Ke (University of Illionis at Chicago) · Bing Liu (University of Illinois, Chicago) · Xingchang Huang (ETH Zurich)

训练前图神经网络:具有增强功能的对比学习框架
Pre-Training Graph Neural Networks: A Contrastive Learning Framework with Augmentations
Yuning You (Texas A&M University) · Tianlong Chen (Unversity of Texas at Austin) · Yongduo Sui (University of Science and Technology of China) · Ting Chen (Google) · Zhangyang Wang (University of Texas at Austin) · Yang Shen (Texas A&M University)

Geo-PIFu:用于单视图人类重构的几何和像素对齐的隐式函数
Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction
Tong He (UCLA) · John Collomosse (Adobe Research) · Hailin Jin (Adobe) · Stefano Soatto (UCLA)

在没有图数据和对抗设置的情况下图卷积网络的变分推理
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Pantelis Elinas (Data61) · Edwin Bonilla (Data61) · Louis C. Tiao (University of Sydney)

凹凸和背包环境中的约束情节式强化学习
Constrained episodic reinforcement learning in concave-convex and knapsack settings
Kianté Brantley (The University of Maryland College Park) · Miro Dudik (Microsoft Research) · Thodoris Lykouris (Microsoft Research NYC) · Sobhan Miryoosefi (Princeton University) · Max Simchowitz (Berkeley) · Aleksandrs Slivkins (Microsoft Research) · Wen Sun (Microsoft Research NYC)

尾巴攻击:是的,您确实可以进行后门联合学习
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
Hongyi Wang (University of Wisconsin-Madison) · Kartik Sreenivasan (University of Wisconsin-Madison) · Shashank Rajput (University of Wisconsin - Madison) · Harit Vishwakarma (University of Wisconsin Madison) · Jy-yong Sohn (KAIST) · Saurabh Agarwal (UW-Madison) · Kangwook Lee (UW Madison) · Dimitris Papailiopoulos (University of Wisconsin-Madison)

低秩分布的线性样本学习
Linear-Sample Learning of Low-Rank Distributions
Ayush Jain (UC San Diego) · Alon Orlitsky (University of California, San Diego)

完整的套索权衡图
The Complete Lasso Tradeoff Diagram
Hua Wang (Wharton School, University of Pennsylvania) · Yachong Yang (University of Pennsylvania) · Zhiqi Bu (University of Pennsylvania) · Weijie Su (The Wharton School, University of Pennsylvania)

经认证的图卷积网络在拓扑攻击下的图分类鲁棒性
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks
Hongwei Jin (University of Illinois at Chicago) · Zhan Shi (University of Illinois at Chicago) · Venkata Jaya Shankar Ashish Peruri (University of Illinois at Chicago) · Xinhua Zhang (UIC)

视觉注意力的神经编码
Neural encoding with visual attention
Meenakshi Khosla (Cornell University) · Gia Ngo (Cornell University) · Keith Jamison (Cornell University) · Amy Kuceyeski (Cornell University) · Mert Sabuncu (Cornell)

可解释的投票
Explainable Voting
Dominik Peters (Carnegie Mellon University) · Ariel Procaccia (Harvard University) · Alexandros Psomas (Purdue University) · Zixin Zhou (Peking University)

私人学习与在线学习的计算分离
A Computational Separation between Private Learning and Online Learning
Mark Bun (Boston University)

用于Minimax优化的Catalyst框架
A Catalyst Framework for Minimax Optimization
Junchi Yang (University of Illinois) · Siqi Zhang (University of Illinois at Urbana-Champaign) · Negar Kiyavash (École Polytechnique Fédérale de Lausanne) · Niao He (UIUC)

通过低秩矩阵估计进行有效的样本强化学习
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation
Devavrat Shah (Massachusetts Institute of Technology) · Dogyoon Song (Massachusetts Institute of Technology) · Zhi Xu (MIT) · Yuzhe Yang (MIT)

弱监督视觉语言对立的反事实对比学习
Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding
Zhu Zhang (Zhejiang University) · Zhou Zhao (Zhejiang University) · Zhijie Lin (Zhejiang University) · jieming zhu (Huawei Noah’’s Ark Lab) · Xiuqiang He (Huawei Noah’s Ark Lab)

批量学习的通用方法
A General Method for Robust Learning from Batches
Ayush Jain (UC San Diego) · Alon Orlitsky (University of California, San Diego)

连续稀疏中奖
Winning the Lottery with Continuous Sparsification
Pedro Savarese (TTIC) · Hugo Silva (Independent Researcher) · Michael Maire (University of Chicago)

ShiftAddNet:受硬件启发的深度网络
ShiftAddNet: A Hardware-Inspired Deep Network
Haoran You (Rice University) · Xiaohan Chen (University of Texas at Austin) · Yongan Zhang (Rice University) · Chaojian Li (Rice University) · Sicheng Li (Alibaba group) · Zihao Liu (Alibaba Group) · Zhangyang Wang (University of Texas at Austin) · Yingyan Lin (Rice University)

用于深度正则化的近端映射
Proximal Mapping for Deep Regularization
mao li (University of Illinois at Chicago) · Yingyi Ma (University of Illinois at Chicago) · Xinhua Zhang (UIC)

平滑课程
Curriculum By Smoothing
Samarth Sinha (University of Toronto, Vector Institute) · Animesh Garg (Univ. of Toronto, Vector Institute, Nvidia) · Hugo Larochelle (Google Brain)

通过元变换网络嵌入对带有少量新颖标签的图进行节点分类
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
Lin Lan (Xi’an Jiaotong University) · Pinghui Wang (Xi’an Jiaotong University) · Xuefeng Du (Xi’an Jiaotong University) · Kaikai Song (Huawei Noah’s Ark Lab) · Jing Tao (Xi’an Jiaotong University) · Xiaohong Guan (Xi’an Jiaotong University)

图形模型约束优化的新方法
A Novel Approach for Constrained Optimization in Graphical Models
Sara Rouhani (University of Texas at Dallas) · Tahrima Rahman (UT Dallas) · Vibhav Gogate (UT Dallas)

强化学习中动力学一般化的轨迹选择式学习
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Younggyo Seo (KAIST) · Kimin Lee (UC Berkeley) · Ignasi Clavera Gilaberte (UC Berkeley) · Thanard Kurutach (University of California Berkeley) · Jinwoo Shin (KAIST) · Pieter Abbeel (UC Berkeley & covariant.ai)

随机重排:简化分析并进行大量改进
Random Reshuffling: Simple Analysis with Vast Improvements
Konstantin Mishchenko (KAUST) · Ahmed Khaled Ragab Bayoumi (Cairo University) · Peter Richtarik (KAUST)

随机收集的最坏情况数据
Randomly Collected, Worst Case Data
Justin Chen (Stanford) · Gregory Valiant (Stanford University) · Paul Valiant (IAS; Purdue University)

合作异构深度强化学习
Cooperative Heterogeneous Deep Reinforcement Learning
Han Zheng (UTS) · Pengfei Wei (National University of Singapore) · Jing Jiang (University of Technology Sydney) · Guodong Long (University of Technology Sydney (UTS)) · Qinghua Lu (Data61, CSIRO) · Chengqi Zhang (University of Technology Sydney)

使用噪声探测递归神经网络结构和修剪突触
Using noise to probe recurrent neural network structure and prune synapses
Eli Moore (University of California, Davis) · Rishidev Chaudhuri (University of California, Davis)

了解具有推理层的深度架构
Understanding Deep Architecture with Reasoning Layer
Xinshi Chen (Georgia Institution of Technology) · Yufei Zhang (University of Oxford) · Christoph Reisinger (University of Oxford) · Le Song (Georgia Institute of Technology)

O(n)连接足以表达:稀疏变压器的通用近似性
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers
Chulhee Yun (MIT) · Yin-Wen Chang (Google Inc.) · Srinadh Bhojanapalli (Google AI) · Ankit Singh Rawat (Google Research) · Sashank Reddi (Google) · Sanjiv Kumar (Google Research)

Adam与深度学习的强盗采样
Adam with Bandit Sampling for Deep Learning
Rui Liu (University of Michigan, Ann Arbor) · Tianyi Wu (University of Michigan, Ann Arbor) · Barzan Mozafari (University of Michigan)

通过离散无向图形模型的集成进行对抗性学习的推理
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models
Adarsh K Jeewajee (MIT) · Leslie Kaelbling (MIT)

通过计算复杂性的角度解释模型的可解释性
Model Interpretability through the lens of Computational Complexity
Pablo Barceló (PUC Chile & Millenium Instititute for Foundational Research on Data) · Mikaël Monet (Millenium Instititute for Foundational Research on Data) · Jorge Pérez (Universidad de Chile) · Bernardo Subercaseaux (Universidad de Chiel)

斯坦因自我排斥动力学:以往样品的好处
Stein Self-Repulsive Dynamics: Benefits From Past Samples
Mao Ye (The University of Texas at Austin) · Tongzheng Ren (UT Austin) · Qiang Liu (UT Austin)

约束马尔可夫决策过程的自然原始对偶方法的全局收敛性
Global Convergence of Natural Primal-Dual Method for Constrained Markov Decision Processes
Dongsheng Ding (University of Southern California) · Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Mihailo Jovanovic (University of Southern California) · Tamer Basar (University of Illinois at Urbana-Champaign)

无需任务的连续元学习
Continuous Meta-Learning without Tasks
James Harrison (Stanford University) · Apoorva Sharma (Stanford University) · Chelsea Finn (Stanford) · Marco Pavone (Stanford University)

FleXOR:可训练的分数量化
FleXOR: Trainable Fractional Quantization
Dongsoo Lee (Samsung Research) · Se Jung Kwon (Samsung Research) · Byeongwook Kim (Samsung Research) · Yongkweon Jeon (Samsung Research) · Baeseong Park (samsung research) · Jeongin Yun (Samsung Research)

通过近似逆敏感性机制实现差异隐私中的实例最优性
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms
Hilal Asi (Stanford University) · John Duchi (Stanford)

拉普拉斯结构图形模型下的非凸稀疏图学习
Nonconvex Sparse Graph Learning under Laplacian-structured Graphical Model
Jiaxi Ying (The Hong Kong University of Science and Technology) · José Vinícius de Miranda Cardoso (HKUST) · Daniel Palomar (The Hong Kong University of Science and Technology)

带有亚分析式对比学习的短视视觉推理
Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning
Youngsung Kim (Samsung Advanced Institute of Technology) · Jinwoo Shin (KAIST) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics) · Sung Ju Hwang (KAIST, AITRICS)

MATE:将模型意识插入到用于元学习的任务嵌入中
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning
Xiaohan Chen (University of Texas at Austin) · Zhangyang Wang (University of Texas at Austin) · Siyu Tang (ETH Zurich) · Krikamol Muandet (Max Planck Institute for Intelligent Systems)

跨语言检索以进行反复的自我监督训练
Cross-lingual Retrieval for Iterative Self-Supervised Training
Chau Tran (Facebook Inc) · Yuqing Tang (Facebook AI) · Xian Li (Facebook) · Jiatao Gu (Facebook AI Research)

互斥性是深度神经网络的挑战
Mutual exclusivity as a challenge for deep neural networks
Kanishk Gandhi (New York University) · Brenden Lake (New York University)

具有再现性的未知物种数量的最佳预测
Optimal Prediction of the Number of Unseen Species with Reproducibility
Yi Hao (University of California, San Diego) · Ping Li (Baidu Research USA)

讲授预训练模型以系统地推理内隐知识
Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
Alon Talmor (Allen Institute for AI, Tel Aviv University) · Oyvind Tafjord (Allen Institute for AI) · Peter Clark (Allen Institute for AI) · Yoav Goldberg (Allen Institute for AI, Bar Ilan University) · Jonathan Berant (Tel Aviv University)

通过贪婪优化进行网络修剪:快速且高效的算法
Network Pruning via Greedy Optimization: Fast Rate and Efficient Algorithms
Mao Ye (The University of Texas at Austin) · Lemeng Wu (UT Austin) · Qiang Liu (UT Austin)

神经复杂性测度
Neural Complexity Measures
Yoonho Lee (AITRICS) · Juho Lee (KAIST, AITRICS) · Sung Ju Hwang (KAIST, AITRICS) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics) · Seungjin Choi (POSTECH)

DeepSVG:用于矢量图形动画的分层生成网络
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
Alexandre Carlier (ETH Zurich) · Martin Danelljan (ETH Zurich) · Alexandre Alahi (EPFL) · Radu Timofte (ETH Zurich)

网络压缩中的属性保留,以确保可靠的网络解释
Attribution Preservation in Network Compression for Reliable Network Interpretation
Geondo Park (Korea Advanced Institute of Science and Technology) · June Yong Yang (Korea Advanced Institute of Science and Technology) · Sung Ju Hwang (KAIST, AITRICS) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics)

内隐分布强化学习
Implicit Distributional Reinforcement Learning
Yuguang Yue (University of Texas at Austin) · Zhendong Wang (University of Texas, Austin) · Mingyuan Zhou (University of Texas at Austin)

遗憾的是拥有多个最佳武器
On Regret with Multiple Best Arms
Yinglun Zhu (University of Wisconsin-Madison) · Robert Nowak (University of Wisconsion-Madison)

通过贝叶斯优化对强化学习中奖励功能的有效探索
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
Sreejith Balakrishnan (National University of Singapore) · Quoc Phong Nguyen (National University of Singapore) · Bryan Kian Hsiang Low (National University of Singapore) · Harold Soh (National University Singapore)

带有异构路径元路径的自我监督辅助学习
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Dasol Hwang (Korea University) · Jinyoung Park (Korea University) · Sunyoung Kwon (Pusan National University) · KyungMin Kim (Seoul National University) · Jung-Woo Ha (Clova AI Research, NAVER Corp.) · Hyunwoo Kim (Korea University)

PAC-贝叶斯框架的局限性
A Limitation of the PAC-Bayes Framework
Roi Livni (Tel Aviv University) · Shay Moran (Google AI Princeton)

具有分层图辅助信息的二进制矩阵完成
Binary Matrix Completion with Hierarchical Graph Side Information
Adel Elmahdy (University of Minnesota) · Junhyung Ahn (KAIST) · Changho Suh (KAIST) · Soheil Mohajer (University of Minnesota)

非随机线性强盗的延迟与合作
Delay and Cooperation in Nonstochastic Linear Bandits
Shinji Ito (NEC Corporation) · Daisuke Hatano (RIKEN AIP) · Hanna Sumita (Tokyo Institute of Technology) · Kei Takemura (NEC Corporation) · Takuro Fukunaga (Chuo University, JST PRESTO, RIKEN AIP) · Naonori Kakimura (Keio University) · Ken-Ichi Kawarabayashi (National Institute of Informatics)

从视听空间对齐中学习表示形式
Learning Representations from Audio-Visual Spatial Alignment
Pedro Morgado (University of California, San Diego) · Yi Li (UC San Diego) · Nuno Nvasconcelos (UC San Diego)

通过弹道空间平滑学习指导奖励
Learning Guidance Rewards with Trajectory-space Smoothing
Tanmay Gangwani (University of Illinois, Urbana-Champaign) · Yuan Zhou (UIUC) · Jian Peng (University of Illinois at Urbana-Champaign)

混沌,极端和乐观:游戏学习量分析
Chaos, Extremism and Optimism: Volume Analysis of Learning in Games
Yun Kuen Cheung (Singapore University of Technology and Design) · Georgios Piliouras (Singapore University of Technology and Design)

EPOC:强化学习的正确正确的政策梯度方法
EPOC: A Provably Correct Policy Gradient Approach to Reinforcement Learning
Alekh Agarwal (Microsoft Research) · Mikael Henaff (Microsoft) · Sham Kakade (University of Washington & Microsoft Research) · Wen Sun (Microsoft Research NYC)

深度神经网络低位训练的统计框架
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
Jianfei Chen (RealAI) · Yu Gai (UC Berkeley) · Zhewei Yao (UC Berkeley) · Michael W Mahoney (UC Berkeley) · Joseph Gonzalez (UC Berkeley)

在多人游戏中无悔学习时,紧迫的最后收敛速度
Tight last-iterate convergence rates for no-regret learning in multi-player games
Noah Golowich (Massachusetts Institute of Technology) · Sarath Pattathil (Massachusetts Institute of Technology) · Constantinos Daskalakis (MIT)

轮廓熵:分布的可学习性和可压缩性的基本度量
Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions
Yi Hao (University of California, San Diego) · Alon Orlitsky (University of California, San Diego)

连续时间的团体公平在线分配
Group-Fair Online Allocation in Continuous Time
Semih Cayci (The Ohio State University) · Swati Gupta (Georgia Institute of Technology) · Atilla Eryilmaz ()

学习增强算法的原始对偶方法
The Primal-Dual method for Learning Augmented Algorithms
Etienne Bamas (EPFL) · Andreas Maggiori (EPFL) · Ola Svensson (EPFL)

在线社交网络中的共同曝光最大化
Co-exposure Maximization in Online Social Networks
Sijing Tu (kth royal institute of technology) · Cigdem Aslay (Aarhus University) · Aristides Gionis (KTH Royal Institute of Technology)

感应量子嵌入
Inductive Quantum Embedding
Santosh K. Srivastava (IBM Research AI) · Dinesh Khandelwal (IBM Research AI) · Dhiraj Madan (IBM Research) · Dinesh Garg (IBM Research AI, India) · Hima Karanam (IBM Research AI) · L Venkata Subramaniam (IBM Research AI - India)

具有内核和神经功能近似的有效强化学习
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations
Zhuoran Yang (Princeton) · Chi Jin (Princeton University) · Zhaoran Wang (Northwestern University) · Mengdi Wang (Princeton University) · Michael Jordan (UC Berkeley)

预测性信息加速RL学习
Predictive Information Accelerates Learning in RL
Kuang-Huei Lee (Google) · Ian Fischer (Google) · Anthony Liu (University of Michigan) · Yijie Guo (University of Michigan) · Honglak Lee (Google Brain) · John Canny (UC Berkeley) · Sergio Guadarrama (Google Research)

竞争强化学习的解耦策略梯度方法
Decoupled Policy Gradient Methods for Competitive Reinforcement Learning
Constantinos Daskalakis (MIT) · Dylan Foster (MIT) · Noah Golowich (Massachusetts Institute of Technology)

局部最优传递及其在正无标记学习中的应用
Partial Optimal Tranport with applications on Positive-Unlabeled Learning
Laetitia Chapel (IRISA) · Mokhtar Z. Alaya (LITIS Lab, University Rouen Normandy) · Gilles Gasso (LITIS - INSA de Rouen)

只需选择一个标志:借助梯度符号丢失功能,可以减少深度网络中的梯度冲突
Just Pick a Sign: Reducing Gradient Conflict in Deep Networks with Gradient Sign Dropout
Zhao Chen (Waymo LLC) · Jiquan Ngiam (Google Brain) · Yanping Huang (Google Brain) · Thang Luong (Google Brain) · Henrik Kretzschmar (Waymo) · Yuning Chai (Waymo) · Dragomir Anguelov (Waymo)

学习不规则采样时间序列中的长期依赖性
Learning Long-Term Dependencies in Irregularly-Sampled Time Series
Mathias Lechner (IST Austria) · Ramin Hasani (MIT)

具有对抗性损失的CMDP的高置信度原始-双重强化学习
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss
Shuang Qiu (University of Michigan) · Xiaohan Wei (University of Southern California) · Zhuoran Yang (Princeton) · Jieping Ye (University of Michigan) · Zhaoran Wang (Northwestern University)

带有概率安全屏障证书的不确定性下的多机器人防撞
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates
Wenhao Luo (Carnegie Mellon University) · Wen Sun (Microsoft Research NYC) · Ashish Kapoor (Microsoft)

利用“仁义”差分隐私改进稀疏矢量技术
Improving Sparse Vector Technique with Renyi Differential Privacy
Yuqing Zhu (University of California Santa Barbara) · Yu-Xiang Wang (UC Santa Barbara)

可分的强盗探索
Differentiable Bandit Exploration
Craig Boutilier (Google) · Chih-wei Hsu ( Google Research) · Branislav Kveton (Google Research) · Martin Mladenov (Google) · Csaba Szepesvari (DeepMind / University of Alberta) · Manzil Zaheer (Google Research)

在复制内核Kerin空间中使用运算符值的内核进行学习
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces
Akash Saha (Indian Institute of Technology Bombay) · Balamurugan Palaniappan (Indian Institute of Technology Bombay)

推荐系统的对抗性反事实学习与评估
Adversarial Counterfactual Learning and Evaluation for Recommender System
Da Xu (Walmart Labs) · Chuanwei Ruan (Walmart Labs) · Evren Korpeoglu (Walmart Labs) · Sushant Kumar (Walmart Labs) · Kannan Achan (Walmart Labs)

统一神经网络的基于激活和时序的学习规则
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks
Jinseok Kim (Pohang University of Science and Technology (POSTECH)) · Kyungsu Kim (POSTECH) · Jae-Joon Kim (POSTECH)

SURF:一种简单,通用,健壮,快速的分布式学习算法
SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm
Yi Hao (University of California, San Diego) · Ayush Jain (UC San Diego) · Alon Orlitsky (University of California, San Diego) · Vaishakh Ravindrakumar (UC San Diego)

具有目标条件的层次预测器的长期视觉规划
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
Karl Pertsch (University of Southern California) · Oleh Rybkin (University of Pennsylvania) · Frederik Ebert (UC Berkeley) · Shenghao Zhou (University of Pennsylvania) · Dinesh Jayaraman (University of Pennsylvania) · Chelsea Finn (Stanford) · Sergey Levine (UC Berkeley)

全局最优非凸神经网络训练的广义界:无限维兰格文动力学的运输图估计
Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
Taiji Suzuki (The University of Tokyo/RIKEN-AIP)

非平稳内核学习的稀疏频谱扭曲输入量度
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
Anthony Tompkins (The University of Sydney) · Rafael Oliveira (The University of Sydney) · Fabio Ramos (University of Sydney, NVIDIA)

有效尺寸自适应草图绘制方法,可加快规则化的最小二乘优化
Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization
Jonathan Lacotte (Stanford University) · Mert Pilanci (Stanford)

批量归一化可避免随机初始化的深度网络避免等级崩溃
Batch normalization provably avoids ranks collapse for randomly initialised deep networks
Hadi Daneshmand (Inria) · Jonas Kohler (ETHZ) · Francis Bach (INRIA - Ecole Normale Superieure) · Thomas Hofmann (ETH Zurich) · Aurelien Lucchi (ETH Zurich)

深度神经网络中的分层成核
Hierarchical nucleation in deep neural networks
Diego Doimo (International School for Advanced Studies (SISSA)) · Aldo Glielmo (International School for Advanced Studies (SISSA))) · Alessio Ansuini (International School for Advanced Studies (SISSA)) · Alessandro Laio (International School for Advanced Studies (SISSA))

零和马尔可夫博弈中基于模型的多智能体RL,样本复杂度接近最佳
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity
Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Sham Kakade (University of Washington & Microsoft Research) · Tamer Basar (University of Illinois at Urbana-Champaign) · Lin Yang (UCLA)

通过自动化在线实验为生产系统选择模型
Model Selection for Production System via Automated Online Experiments
Zhenwen Dai (Spotify) · Praveen Chandar (Spotify) · Ghazal Fazelnia (Spotify Research) · Benjamin Carterette (Spotify) · Mounia Lalmas (Spotify)

图几何互动学习
Graph Geometry Interaction Learning
Shichao Zhu (Institute of Information Engineering, Chinese Academy of Sciences) · Shirui Pan (Monash University) · Chuan Zhou (Chinese Academy of Sciences) · Jia Wu (Macquarie University) · Yanan Cao (Institute of Information Engineering, Chinese Academy of Sciences) · Bin Wang (Xiaomi AI Lab)

一种用于单倍型组装和病毒拟种重建的卷积自动编码器
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
Ziqi Ke (University of Texas at Austin) · Haris Vikalo (The University of Texas at Austin)

无需参数调整的鲁棒协变量移位
Robust Covariate Shift without Parameter Tuning
Bijan Mazaheri (California Institute of Technology) · Siddharth Jain (Caltech) · Jehoshua Bruck (Caltech)

为人群计数建模嘈杂注释
Modeling Noisy Annotations for Crowd Counting
Jia Wan (City University of Hong Kong) · Antoni Chan (City University of Hong Kong)

神经稀疏体素场
Neural Sparse Voxel Fields
Lingjie Liu (Max Planck Institute for Informatics) · Jiatao Gu (Facebook AI Research) · Kyaw Zaw Lin (National University of Singapore) · Tat-Seng Chua (National university of Singapore) · Christian Theobalt (MPI Informatik)

联邦加速随机梯度下降
Federated Accelerated Stochastic Gradient Descent
Honglin Yuan (Stanford University) · Tengyu Ma (Stanford University)

光滑正则近似值迭代方案的收敛性
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes
Elena Smirnova (Criteo) · Elvis Dohmatob (Criteo)

打破沟通-隐私-准确性困境
Breaking the Communication-Privacy-Accuracy Trilemma
Wei-Ning Chen (Stanford University) · Peter Kairouz (Google) · Ayfer Ozgur (Stanford University)

通过显式热核学习隐式学习流形
Learning Manifold Implicitly via Explicit Heat-Kernel Learning
Yufan Zhou (University at Buffalo) · Changyou Chen (University at Buffalo) · Jinhui Xu (SUNY at Buffalo)

对抗性强的ImageNet模型传输效果更好吗?
Do Adversarially Robust ImageNet Models Transfer Better?
Hadi Salman (Microsoft Research AI) · Andrew Ilyas (MIT) · Logan Engstrom (MIT) · Ashish Kapoor (Microsoft) · Aleksander Madry (MIT)

一类非凸-非凹极小极大问题的全局收敛性和方差减少
Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems
Junchi Yang (University of Illinois) · Negar Kiyavash (École Polytechnique Fédérale de Lausanne) · Niao He (UIUC)

深度学习的射击公式
A shooting formulation of deep learning
François-Xavier Vialard (University Gustave Eiffel) · Roland Kwitt (University of Salzburg) · Susan Wei (University of Melbourne) · Marc Niethammer (UNC Chapel Hill)

PlanGAN:具有稀疏奖励和多个目标的基于模型的计划
PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals
Henry Charlesworth (University of Warwick) · Giovanni Montana (University of Warwick)

通过混合正则化改进强化学习中的泛化
Improving Generalization in Reinforcement Learning with Mixture Regularization
KAIXIN WANG (National University of Singapore) · Bingyi Kang (National University of Singapore) · Jie Shao (Fudan University) · Jiashi Feng (National University of Singapore)

用于发现神经数据中非欧氏潜在结构的歧管GPLVM
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
Kristopher Jensen (University of Cambridge) · Ta-Chu Kao (University of Cambridge) · Marco Tripodi (MRC) · Guillaume Hennequin (Cambridge)

通过轻松计划进行可扩展的信念传播
Scalable Belief Propagation via Relaxed Scheduling
Vitalii Aksenov (IST Austria) · Dan Alistarh (IST Austria & Neural Magic Inc.) · Janne Korhonen (IST Austria)

使用动态规划通过选择性推理计算最佳变化点的有效p值
Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming
Vo Nguyen Le Duy (Nagoya Institute of Technology / RIKEN) · Hiroki Toda (Nagoya Institute of Technology) · Ryota Sugiyama (Nagoya Institute of Technology) · Ichiro Takeuchi (Nagoya Institute of Technology)

随机Softmax技巧的梯度估计
Gradient Estimation with Stochastic Softmax Tricks
Max Paulus (ETH Zurich) · Dami Choi (University of Toronto) · Daniel Tarlow (Google Brain) · Andreas Krause (ETH Zurich) · Chris J. Maddison (University of Toronto)

小心!运动正在模糊您的深度神经网络的视觉
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks
Qing Guo (Nanyang Technological University) · Felix Juefei-Xu (Alibaba Group) · Xiaofei Xie (Nanyang Technological University) · Lei Ma (Kyushu University, Japan) · Jian Wang (Nanyang Technological University) · Bing Yu (Kyushu university) · Wei Feng (Tianjin University) · Yang Liu (Nanyang Technology University, Singapore)

热力学变分目标的高斯过程土匪优化
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Vu Nguyen (University of Oxford) · Vaden Masrani (University of British Columbia) · Rob Brekelmans (University of Southern California) · Michael A Osborne (U Oxford) · Frank Wood (University of British Columbia)

通过对抗性加权学习实现无人口统计学的公平
Fairness without Demographics through Adversarially Reweighted Learning
Preethi Lahoti (Max Planck Institute for Informatics, Germany) · Alex Beutel (Google) · Jilin Chen (Google Brain) · Kang Lee (Google Research) · Flavien Prost (Google) · Nithum Thain (Google) · Xuezhi Wang (Google) · Ed Chi (Google Inc.)

神经动力装置
Neural Power Units
Niklas Maximilian Heim (Czech Technical University) · Tomas Pevny (Czech Technical University) · Vasek Smidl (Czech Technical University in Prague)

RD2:奖励分解与表示分解
RD2: Reward Decomposition with Representation Decomposition
Zichuan Lin (Tsinghua University) · Derek Yang (UC San Diego) · Li Zhao (Microsoft Research) · Tao Qin (Microsoft Research) · Guangwen Yang (Tsinghua University) · Tie-Yan Liu (Microsoft Research Asia)

无限可能的联合对比学习
Joint Contrastive Learning with Infinite Possibilities
Qi Cai (University of Science and Technology of China) · Yu Wang (JD AI Research) · Yingwei Pan (JD AI Research) · Ting Yao (JD AI Research) · Tao Mei (AI Research of JD.com)

学习公平和可转让代表
Learning Fair and Transferable Representations
Luca Oneto (University of Genoa) · Michele Donini (Amazon) · Giulia Luise (University College London) · Carlo Ciliberto (Imperial College London) · Massimiliano Pontil (IIT) · Andreas Maurer ()

了解培训制度在持续学习中的作用
Understanding the Role of Training Regimes in Continual Learning
Seyed Iman Mirzadeh (Washington State University) · Mehrdad Farajtabar (DeepMind) · Razvan Pascanu (Google DeepMind) · Hassan Ghasemzadeh (Washington State University)

非凸问题中随机梯度下降的几乎肯定收敛
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
Panayotis Mertikopoulos (CNRS (French National Center for Scientific Research)) · Nadav Hallak (EPFL) · Ali Kavis (EPFL) · Volkan Cevher (EPFL)

二次采样随机Hadamard变换的最优迭代素描方法
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform
Jonathan Lacotte (Stanford University) · Sifan Liu (Stanford University) · Edgar Dobriban (University of Pennsylvania) · Mert Pilanci (Stanford)

残留蒸馏:迈向没有捷径的便携式深度神经网络
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
Guilin Li (Huawei Noah’s Ark Lab) · Junlei Zhang (Huawei Noah’s Ark Lab) · Yunhe Wang (Huawei Noah’s Ark Lab) · Chuanjian Liu (Huawei Noah’s Ark Lab) · Matthias Tan (CityU) · Yunfeng Lin (Shanghai Jiao Tong University) · Wei Zhang (Noah’s Ark Lab, Huawei Inc.) · Jiashi Feng (National University of Singapore) · Tong Zhang (Tencent AI Lab)

对比生成对抗网络
Contrastive Generative Adversarial Networks
Minguk Kang (POSTECH) · Jaesik Park (POSTECH)

(De)随机化平滑处理,可有效防御补丁攻击
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks
Alexander Levine (University of Maryland, College Park) · Soheil Feizi (University of Maryland)

您认为会发生什么?通过预期结果解释座席行为
What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes
Ho Man Herman Yau (University of Surrey) · Chris Russell (The Alan Turing Institute/ The University of Surrey) · Simon Hadfield (University of Surrey)

多领域分类数据的领域学习
Field-wise Learning for Multi-field Categorical Data
Zhibin Li (University of Technology Sydney ) · Jian Zhang (UTS) · Yongshun Gong (University of Technology Sydney) · Yazhou Yao (Nanjing University of Science and Technology) · Qiang Wu (University of Technology Sydney)

神经网络记住什么以及为什么:通过影响估计发现长尾巴
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Vitaly Feldman (Google Brain) · Chiyuan Zhang (Google Brain)

基于多智能体强化学习的公共池资源管理网络系统控制的博弈分析
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
Arnu Pretorius (InstaDeep) · Scott Cameron (Instadeep) · Elan van Biljon (Stellenbosch University) · Thomas Makkink (InstaDeep) · Shahil Mawjee (InstaDeep) · Jeremy du Plessis (University of Cape Town) · Jonathan Shock (University of Cape Town) · Alexandre Laterre (InstaDeep) · Karim Beguir (InstaDeep)

用于语言组成泛化的分层Poset解码
Hierarchical Poset Decoding for Compositional Generalization in Language
Yinuo Guo (Peking University) · Zeqi Lin (Microsoft) · Jian-Guang Lou (Microsoft) · Dongmei Zhang (Microsoft Research)

传统优化分析与现代深度学习的融合:内在学习率
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate
Zhiyuan Li (Princeton University) · Kaifeng Lyu (Tsinghua University) · Sanjeev Arora (Princeton University)

具有背包约束的快速自适应非单调子模最大化
Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint
Georgios Amanatidis (University of Essex) · Federico Fusco (Sapienza University of Rome) · Philip Lazos (Sapienza University of Rome) · Stefano Leonardi (Sapienza University of Rome) · Rebecca Reiffenhäuser (Sapienza University of Rome)

内核一致性风险估计器:根据训练数据进行风险预测
Kernel Alignment Risk Estimator: Risk Prediction from Training Data
Arthur Jacot (EPFL) · Berfin Simsek (EPFL) · Francesco Spadaro (EPFL) · Clement Hongler (EPFL) · Franck Gabriel (EPFL)

差距依赖的样本复杂度的马尔可夫决策过程中的规划
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity
Anders Jonsson (Universitat Pompeu Fabra) · Emilie Kaufmann (CNRS) · Pierre Menard (Inria) · Omar Darwiche Domingues (Inria) · Edouard Leurent (INRIA) · Michal Valko (DeepMind)

规范3D变形器贴图:统一的参数和非参数方法,用于密集的弱监督类别重构
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
David Novotny (Facebook AI Research) · Roman Shapovalov (Facebook AI Research) · Andrea Vedaldi (University of Oxford / Facebook AI Research)

用于训练图神经网络的Bandit采样器
Bandit Samplers for Training Graph Neural Networks
Ziqi Liu (Ant Financial) · Zhengwei Wu (Ant Financial) · Zhiqiang Zhang (Ant Financial Services Group) · Jun Zhou (Ant Financial) · Shuang Yang (Ant Financial) · Le Song (Ant Financial Services Group) · Yuan Qi (Ant Financial Services Group)

内在动机探索的潜在世界模型
Latent World Models For Intrinsically Motivated Exploration
Aleksandr Ermolov (University of Trento) · Nicu Sebe (University of Trento)

自我监督视频表示学习的循环对比度
Cycle-Contrast for Self-Supervised Video Representation Learning
Quan Kong (Hitachi,Ltd.) · Wenpeng Wei (Hitachi, Ltd.) · Ziwei Deng (Hitachi,Ltd.) · Tomoaki Yoshinaga (Hitachi, Ltd.) · Tomokazu Murakami (Hitachi,Ltd.)

基于文本的游戏具有分层层次注意的深度强化学习
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
Yunqiu Xu (University of Technology Sydney) · Meng Fang (Tencent) · Ling Chen (“ University of Technology, Sydney, Australia”) · Yali Du (University College London) · Joey Tianyi Zhou (IHPC, A*STAR) · Chengqi Zhang (University of Technology Sydney)

基于实例的逼近来分析最大似然
Instance Based Approximations to Profile Maximum Likelihood
Nima Anari (Stanford) · Moses Charikar (Stanford University) · Kirankumar Shiragur (Stanford University) · Aaron Sidford (Stanford)

CoMIR:用于注册的对比多模态图像表示
CoMIR: Contrastive Multimodal Image Representation for Registration
Nicolas Pielawski (Uppsala University) · Elisabeth Wetzer (Centre for Image Analysis, Department of Information Technology, Uppsala University, Sweden) · Johan Öfverstedt (Department of Information Technology, Uppsala University) · Jiahao Lu (Uppsala University) · Carolina Wählby (Uppsala University) · Joakim Lindblad (Centre for Image Analysis, Department of Information Technology, Uppsala University, Sweden) · Natasa Sladoje (Centre for Image Analysis, Department of Information Technology, Uppsala University, Sweden)

虚构的视觉和语言导航:揭露看不见的东西
Counterfactual Vision-and-Language Navigation: Unravelling the Unseen
Amin Parvaneh (University of Adelaide) · Ehsan Abbasnejad (University of Adelaide) · Damien Teney (University of Adelaide) · Qinfeng Shi (University of Adelaide) · Anton van den Hengel (University of Adelaide)

通过棱镜的语言:一种用于多尺度语言表示的光谱方法
Language Through a Prism: A Spectral Approach for Multiscale Language Representations
Alex Tamkin (Stanford University) · Dan Jurafsky (Stanford University) · Noah Goodman (Stanford University)

一般继续学习的黑暗体验:强大而简单的基准
Dark Experience for General Continual Learning: a Strong, Simple Baseline
Pietro Buzzega (University of Modena and Reggio Emilia) · Matteo Boschini (University of Modena and Reggio Emilia) · Angelo Porrello (University of Modena and Reggio Emilia) · Davide Abati (University of Modena and Reggio Emilia) · SIMONE CALDERARA (University of Modena and Reggio Emilia, Italy)

双向卷积泊松伽玛动力系统
Bidirectional Convolutional Poisson Gamma Dynamical Systems
wenchao chen (Xidian university) · Chaojie Wang (Xidian University) · Bo Chen (Xidian University) · Yicheng Liu (Xidian university) · Hao Zhang (Xidian University) · Mingyuan Zhou (University of Texas at Austin)

3D多实体:使可能的3D人体模型集适应模糊的图像数据
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
Benjamin Biggs (University of Cambridge) · David Novotny (Facebook AI Research) · Sebastien Ehrhardt (University of Oxford) · Hanbyul Joo (FAIR) · Ben Graham (Facebook Research) · Andrea Vedaldi (University of Oxford / Facebook AI Research)

语言模型是学习者很少
Language Models are Few-Shot Learners
Tom B Brown (Google Brain) · Benjamin Mann (OpenAI) · Nick Ryder (OpenAI) · Melanie Subbiah (OpenAI) · Jared D Kaplan (Johns Hopkins University) · Prafulla Dhariwal (OpenAI) · Arvind Neelakantan (OpenAI) · Pranav Shyam (OpenAI) · Girish Sastry (OpenAI) · Amanda Askell (OpenAI) · Sandhini Agarwal (OpenAI) · Ariel Herbert-Voss (OpenAI) · Gretchen M Krueger (OpenAI) · Tom Henighan (OpenAI) · Rewon Child (OpenAI) · Aditya Ramesh (OpenAI) · Daniel Ziegler (OpenAI) · Jeffrey Wu (OpenAI) · Clemens Winter (OpenAI) · Chris Hesse (OpenAI) · Mark Chen (OpenAI) · Eric Sigler (OpenAI) · Mateusz Litwin (OpenAI) · Scott Gray (OpenAI) · Benjamin Chess (OpenAI) · Jack Clark (OpenAI) · Christopher Berner (OpenAI) · Sam McCandlish (OpenAI) · Alec Radford (OpenAI) · Ilya Sutskever (OpenAI) · Dario Amodei (OpenAI)

具有先行政策的在线计划
Online Planning with Lookahead Policies
Yonathan Efroni (Technion) · Mohammad Ghavamzadeh (Google Research) · Shie Mannor (Technion)

变分贝叶斯学习
Variational Bayesian Unlearning
Quoc Phong Nguyen (National University of Singapore) · Bryan Kian Hsiang Low (National University of Singapore) · Patrick Jaillet (MIT)

通过Langevin Dynamics的对抗训练进行强大的强化学习
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
Parameswaran Kamalaruban (EPFL) · Yu-Ting Huang (EPFL) · Ya-Ping Hsieh (EPFL) · Paul Rolland (EPFL) · Cheng Shi (Unversity of Basel) · Volkan Cevher (EPFL)

论Louvain对图聚类的作用
On the Power of Louvain for Graph Clustering
Vincent Cohen-Addad (CNRS & Sorbonne Université) · Adrian Kosowski (NavAlgo) · Frederik Mallmann-Trenn (King’s College London) · David Saulpic (Ecole normale supérieure)

Interferobot:通过强化学习剂对准光学干涉仪
Interferobot: aligning an optical interferometer by a reinforcement learning agent
Dmitry Sorokin (Russian Quantum Center) · Alexander Ulanov (Russian Quantum Center) · Ekaterina Sazhina (Russian Quantum Center) · Alexander Lvovsky (Oxford University)

卷积神经过程的元学习平稳随机过程预测
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Andrew Foong (University of Cambridge) · Wessel Bruinsma (Invenia Labs and University of Cambridge) · Jonathan Gordon (University of Cambridge) · Yann Dubois (Facebook AI Research) · James Requeima (University of Cambridge / Invenia Labs) · Richard E Turner (University of Cambridge)

以插槽为中心的以对象为中心的学习
Object-Centric Learning with Slot Attention
Francesco Locatello (ETH Zürich - MPI Tübingen) · Dirk Weissenborn (Google) · Thomas Unterthiner (Google Research, Brain Team) · Aravindh Mahendran (Google) · Georg Heigold (Google) · Jakob Uszkoreit (Google, Inc.) · Alexey Dosovitskiy (Google Research) · Thomas Kipf (Google Research)

后验网络:通过基于密度的伪计数无OOD样本的不确定度估计
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
Bertrand Charpentier (Technical University of Munich) · Daniel Zügner (Technical University of Munich) · Stephan Günnemann (Technical University of Munich)

保证金不足以解释梯度提升
Margins are Insufficient for Explaining Gradient Boosting
Allan Grønlund (Aarhus University, MADALGO) · Lior Kamma (Aarhus University) · Kasper Green Larsen (Aarhus University)

引导自己的潜能-自我监督学习的新方法
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning
Jean-Bastien Grill (DeepMind) · Florian Strub (DeepMind) · Florent Altché (DeepMind) · Corentin Tallec (Deepmind) · Pierre Richemond (Imperial College) · Elena Buchatskaya (DeepMind) · Carl Doersch (DeepMind) · Bernardo Avila Pires (DeepMind) · Zhaohan Guo (DeepMind) · Mohammad Gheshlaghi Azar (DeepMind) · Bilal Piot (DeepMind) · koray kavukcuoglu (DeepMind) · Remi Munos (DeepMind) · Michal Valko (DeepMind)

视觉问答中用于组合泛化的多峰图网络
Multimodal Graph Networks for Compositional Generalization in Visual Question Answering
Raeid Saqur (Princeton University) · Karthik Narasimhan (Princeton University)

增强学习以实现多频率控制
Reinforcement Learning for Control with Multiple Frequencies
Jongmin Lee (KAIST) · ByungJun Lee (KAIST) · Kee-Eung Kim (KAIST)

CrossTransformers:具有空间感知能力的一次性拍摄
CrossTransformers: spatially-aware few-shot transfer
Carl Doersch (DeepMind) · Ankush Gupta (DeepMind) · Andrew Zisserman (DeepMind & University of Oxford)

神经路径特征和神经路径内核:了解门在深度学习中的作用
Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning
Chandrashekar Lakshminarayanan (Indian Institute of Technology, Palakkad) · Amit Vikram Singh (Indian Institute Of Technology, Palakkad)

贝叶斯神经网络中近似推断的表达性
On the Expressiveness of Approximate Inference in Bayesian Neural Networks
Andrew Foong (University of Cambridge) · David Burt (University of Cambridge) · Yingzhen Li (Microsoft Research Cambridge) · Richard E Turner (University of Cambridge)

两层神经网络的广义神经正切核分析
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks
Zixiang Chen (UCLA) · Yuan Cao (UCLA) · Quanquan Gu (UCLA) · Tong Zhang (Tencent AI Lab)

用于不平衡半监督学习的伪标签分布对齐精炼厂
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning
Jaehyung Kim (KAIST) · Youngbum Hur (Samsung Advanced Institute of Technology) · Sejun Park (KAIST) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics) · Sung Ju Hwang (KAIST, AITRICS) · Jinwoo Shin (KAIST)

SMYRF-使用非对称聚类有效关注
SMYRF - Efficient attention using asymmetric clustering
Giannis Daras (National Technical University of Athens) · Nikita Kitaev (University of California, Berkeley) · Augustus Odena (Google Brain) · Alexandros Dimakis (University of Texas, Austin)

了解和改进快速对抗训练
Understanding and Improving Fast Adversarial Training
Maksym Andriushchenko (EPFL) · Nicolas Flammarion (EPFL)

最小最大优化的最佳历时随机梯度下降法
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization
Yan Yan (the University of Iowa) · Yi Xu (Alibaba Group U.S. Inc.) · Qihang Lin (University of Iowa) · Wei Liu (Tencent AI Lab) · Tianbao Yang (The University of Iowa)

学习具有少量潜在变量的受限玻尔兹曼机器
Learning Restricted Boltzmann Machines with Few Latent Variables
Guy Bresler (MIT) · Rares-Darius Buhai (ETH Zurich)

鄂尔多斯走向神经:图组合优化的无监督学习框架
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
Nikolaos Karalias (EPFL) · Andreas Loukas (EPFL)

DynaBERT:具有自适应宽度和深度的动态BERT
DynaBERT: Dynamic BERT with Adaptive Width and Depth
Lu Hou (Huawei Noah’s Ark Lab) · Zhiqi Huang (Peking University) · Lifeng Shang (Huawei Noah’s Ark Lab) · Xin Jiang (Huawei Noah’s Ark Lab) · Xiao Chen (Huawei Noah’s Ark Lab) · Qun Liu (Huawei Noah’s Ark Lab)

反向梯度-在联合学习中打破隐私有多容易?
Inverting Gradients - How easy is it to break privacy in federated learning?
Jonas Geiping (University of Siegen) · Hartmut Bauermeister (University of Siegen) · Hannah Dröge (University of Siegen) · Michael Moeller (University of Siegen)

通过动态实例硬度指导学习
Guide Learning by Dynamic Instance Hardness
Tianyi Zhou (University of Washington, Seattle) · Shengjie Wang (“University of Washington, Seattle”) · Jeff Bilmes (University of Washington, Seattle)

MinMax优化运输方法:正则化,逼近和数值
MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics
Luca De Gennaro Aquino (Grenoble Ecole de Management) · Stephan Eckstein (University of Konstanz)

MMA正则化:通过最大化最小角度来减少神经网络的权重
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles
Zhennan Wang (Shenzhen University) · Canqun Xiang (Shenzhen University) · Wenbin Zou (Shenzhen University) · Chen Xu (Shenzhen University)

变分自动编码器的递归推理
Recursive Inference for Variational Autoencoders
Minyoung Kim (Samsung AI Center Cambridge) · Vladimir Pavlovic (Rutgers University)

公平的决定如何体现长期资格?
How do fair decisions fare in long-term qualification?
Xueru Zhang (University of Michigan) · Ruibo Tu (KTH Royal Institute of Technology) · Yang Liu (UC Santa Cruz) · mingyan liu (university of Michigan, Ann Arbor) · Hedvig Kjellstrom (KTH Royal Institute of Technology) · Kun Zhang (CMU) · Cheng Zhang (Microsoft Research, Cambridge, UK)

合唱团
Collegial Ensembles
Etai Littwin (Apple) · Ben Myara (apple) · Sima Sabah (Apple) · Joshua M Susskind (Apple Inc.) · Shuangfei Zhai (Apple) · Oren Golan (apple)

学习玩顺序游戏与未知对手
Learning to Play Sequential Games versus Unknown Opponents
Pier Giuseppe Sessa (ETH Zürich) · Ilija Bogunovic (ETH Zurich) · Maryam Kamgarpour (ETH Zürich) · Andreas Krause (ETH Zurich)

低秩正交子空间中的持续学习
Continual Learning in Low-rank Orthogonal Subspaces
Arslan Chaudhry (University of Oxford) · Naeemullah Khan (University of Oxford) · Puneet Dokania (University of Oxford) · Philip Torr (University of Oxford)

随机归一化流
Stochastic Normalizing Flows
Hao Wu (Freie Universität Berlin) · Jonas Köhler (Freie Universität Berlin) · Frank Noe (FU Berlin)

情境游戏:附带信息的多智能体学习
Contextual Games: Multi-Agent Learning with Side Information
Pier Giuseppe Sessa (ETH Zürich) · Ilija Bogunovic (ETH Zurich) · Andreas Krause (ETH Zurich) · Maryam Kamgarpour (ETH Zürich)

用于多类神经网络校准的保留顺序内函数
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
Amir Rahimi (Australian National University) · Amirreza Shaban (Georgia Institute of Technology) · Ching-An Cheng (Microsoft) · Richard I Hartley (Australian National University) · Byron Boots (University of Washington)

贝叶斯鲁棒优化的模仿学习
Bayesian Robust Optimization for Imitation Learning
Daniel Brown (The University of Texas at Austin) · Scott Niekum (UT Austin) · Marek Petrik (University of New Hampshire)

通过简单有效的正则化方法提高表达策略的随机性
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method
Qi Zhou (University of Science and Technology of China) · Yufei Kuang (University of Science and Technology of China) · Zherui Qiu (University of Science and Technology of China) · Houqiang Li (University of Science and Technology of China) · Jie Wang (University of Science and Technology of China)

在时间序列预测中对深度学习可解释性进行基准测试
Benchmarking Deep Learning Interpretability in Time Series Predictions
Aya Abdelsalam Ismail (University of Maryland) · Mohamed Gunady (University of Maryland) · Hector Corrada Bravo (University of Maryland) · Soheil Feizi (University of Maryland)

Nimble:在GPU上轻量级执行深度神经网络
Nimble: Lightweight Execution of Deep Neural Networks on a GPU
Woosuk Kwon (Seoul National University) · Gyeong-In Yu (Seoul National University) · Eunji Jeong (Seoul National Univerity) · Byung-Gon Chun (Seoul National University)

教GAN不该学习的内容
Teaching a GAN What Not to Learn
Siddarth Asokan (Indian Institute of Science) · Chandra Seelamantula (IISc Bangalore)

使用时间分层一类网络的时间序列异常检测
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network
Lifeng Shen (The Hong Kong University of Science and Technology) · Zhuocong Li (Tencent) · James Kwok (Hong Kong University of Science and Technology)

通过拒绝采样实现快速准确的k-means ++
Fast and Accurate k-means++ via Rejection Sampling
Vincent Cohen-Addad (CNRS & Sorbonne Université) · Silvio Lattanzi (Google Research) · Ashkan Norouzi-Fard (Google Research) · Christian Sohler (University of Cologne) · Ola Svensson (EPFL)

因子图神经网络
Factor Graph Neural Networks
Zhen Zhang (University of Adelaide) · Fan Wu (Nanjing University) · Wee Sun Lee (National University of Singapore)

风险敏感型强化学习:后悔中接近最佳的风险样本权衡
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret
Yingjie Fei (Cornell University) · Zhuoran Yang (Princeton) · Yudong Chen (Cornell University) · Zhaoran Wang (Northwestern University) · Qiaomin Xie (Cornell University)

对递归模型中的排列不变性进行正则化
Regularizing Towards Permutation Invariance In Recurrent Models
Edo Cohen-Karlik (Tel Aviv University) · Avichai Ben David (Tel Aviv University) · Amir Globerson (Tel Aviv University, Google)

深阿基米德科普拉斯
Deep Archimedean Copulas
Chun Kai Ling (Carnegie Mellon University) · Fei Fang (Carnegie Mellon University) · J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

特征变换上最近邻分类器的收敛性
On Convergence of Nearest Neighbor Classifiers over Feature Transformations
Luka Rimanic (ETH Zurich) · Cedric Renggli (ETH Zurich) · Bo Li (UIUC) · Ce Zhang (ETH Zurich)

权衡个性化以提高准确性:协同过滤中的数据调试
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering
Long Chen (Nanjing University) · Yuan Yao (Nanjing University) · Hanghang Tong (University of Illinois at Urbana-Champaign) · Miao Xu (RIKEN AIP) · Feng Xu (Nanjing University)

非负函数的非参数模型
Non-parametric Models for Non-negative Functions
Ulysse Marteau-Ferey (DI ENS / INRIA) · Francis Bach (INRIA - Ecole Normale Superieure) · Alessandro Rudi (INRIA, Ecole Normale Superieure)

在线Sinkhorn:与样品流的最佳运输距离
Online Sinkhorn: Optimal Transport distances from sample streams
Arthur Mensch (ENS) · Gabriel Peyré (CNRS and ENS)

使用门控线性网络在强盗中进行在线学习
Online Learning in Contextual Bandits using Gated Linear Networks
Eren Sezener (DeepMind) · Marcus Hutter (DeepMind) · David Budden (DeepMind) · Jianan Wang (DeepMind) · Joel Veness (Deepmind)

自适应学习从聚焦和散焦的图像对中删除
Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs
Lin Liu (University of Science and Technology of China) · Shanxin Yuan (Huawei Technologies Research and Development (UK)) · Jianzhuang Liu (Huawei Noah’s Ark Lab) · Liping Bao (University of Science and Technology of China) · Gregory Slabaugh (Huawei Noah’s Ark Lab) · Qi Tian (Huawei Noah’s Ark Lab)

汤普森抽样的随机局部监测分析与设计
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring
Taira Tsuchiya (The University of Tokyo) · Junya Honda (The Univerisity of Tokyo / RIKEN) · Masashi Sugiyama (RIKEN / University of Tokyo)

Besov IPM损失下的鲁棒密度估计
Robust Density Estimation under Besov IPM Losses
Ananya Uppal (Carnegie Mellon University) · Shashank Singh (Google) · Barnabas Poczos (Carnegie Mellon University)

落后数据流水线的随机优化
Stochastic Optimization with Laggard Data Pipelines
Naman Agarwal (Google) · Rohan Anil (Google) · Tomer Koren (Google) · Kunal Talwar (Google) · Cyril Zhang (Princeton University)

学习使用1百万像素事件摄像机检测物体
Learning to Detect Objects with a 1 Megapixel Event Camera
Etienne Perot (PROPHESEE) · Pierre de Tournemire (PROPHESEE) · Davide Nitti (PROPHESEE) · Jonathan Masci (NNAISENSE) · Amos Sironi (PROPHESEE)

伸缩密度比估计
Telescoping Density-Ratio Estimation
Benjamin Rhodes (University of Edinburgh) · Kai Xu (University of Edinburgh) · Michael U. Gutmann (University of Edinburgh)

GS-WGAN:一种用于学习差分私人发电机的梯度消毒方法
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
Dingfan Chen (CISPA - Helmholtz Center for Information Security) · Tribhuvanesh Orekondy (Max Planck Institute for Informatics) · Mario Fritz (CISPA Helmholtz Center i.G.)

阶段凸优化问题的有效非凸重构
An efficient nonconvex reformulation of stagewise convex optimization problems
Srinadh Bhojanapalli (Google AI) · Rudy Bunel (Deepmind) · Krishnamurthy Dvijotham (DeepMind) · Oliver Hinder (University of Pittsburgh)

矩阵Fisher分布的概率方向估计
Probabilistic orientation estimation with matrix Fisher distributions
David A Mohlin (KTH) · Josephine Sullivan (KTH Royal Institute of Technology) · Gérald Bianchi (Tobii AB)

从失败中学习:从偏置分类器中消除分类器的偏见
Learning from Failure: De-biasing Classifier from Biased Classifier
Junhyun Nam (KAIST) · Hyuntak Cha (KAIST) · Sung-Soo Ahn (KAIST) · Jaeho Lee (KAIST) · Jinwoo Shin (KAIST)

使用面板数据进行回归的核心集
Coresets for Regressions with Panel Data
Lingxiao Huang (Huawei) · K Sudhir (Yale University) · Nisheeth Vishnoi (Yale University)

语言是好奇心驱使探索中想象目标的认知工具
Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration
Cédric Colas (INRIA) · Tristan Karch (Inria) · Nicolas Lair (Inserm Robot Cognition Lab) · Jean-Michel Dussoux (Cloud Temple) · Clément Moulin-Frier (Inria) · Peter F Dominey (INSERM/CNRS) · Pierre-Yves Oudeyer (INRIA)

具有潜在混杂因素的自相关时间序列的高召回因果发现
High-recall causal discovery for autocorrelated time series with latent confounders
Andreas Gerhardus (German Aerospace Center (DLR)) · Jakob Runge (Institute of Data Science, German Aerospace Center (DLR))

注意的焦点改善了视觉特征中的信息传递
Focus of Attention Improves Information Transfer in Visual Features
Matteo Tiezzi (University of Siena) · Stefano Melacci (University of Siena) · Alessandro Betti (University of Siena) · Marco Maggini (University of Siena) · Marco Gori (University of Siena)

Deep Rao-Blackwellised粒子过滤器,用于时间序列预测
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting
Richard Kurle (Volkswagen Group) · Syama Sundar Rangapuram (Amazon Research) · Emmanuel de Bézenac (Sorbonne Université) · Stephan Günnemann (Technical University of Munich) · Jan Gasthaus (Amazon.com)

专家指导的强化学习,用于离线策略学习和评估
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
Aaron Sonabend (Harvard University) · Junwei Lu () · Leo Anthony Celi (Massachusetts Institute of Technology) · Tianxi Cai (Harvard School of Public Health) · Peter Szolovits (MIT)

通过线性不变嵌入进行对应学习
Correspondence learning via linearly-invariant embedding
Riccardo Marin (University of Verona) · Marie-Julie Rakotosaona (Ecole Polytechnique) · Simone Melzi (University of Verona) · Maks Ovsjanikov (Ecole polytechnique)

用多项式时间和延迟解释朴素贝叶斯和其他线性分类器
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
Joao Marques-Silva (ANITI, Federal University of Toulouse Midi-Pyrénées) · Thomas Gerspacher (ANITI) · Martin Cooper (University of Toulouse 3) · Alexey Ignatiev (Monash University) · Nina Narodytska (VMmare Research)

社区检测对随机几何扰动的鲁棒性
Robustness of Community Detection to Random Geometric Perturbations
Sandrine Peche (LPSM, Université Paris Diderot) · Vianney Perchet (ENSAE & Criteo AI Lab)

基于自然图像中目标可检测性模型的最佳视觉搜索
Optimal visual search based on a model of target detectability in natural images
SHIMA RASHIDI (University of Melbourne) · Krista A Ehinger (The University of Melbourne) · Andrew Turpin (University of Melbourne) · Lars Kulik (University of Melbourne)

面向分类的随机森林收敛速率分析
Towards Convergence Rate Analysis of Random Forests for Classification
Wei Gao (Nanjing University) · Zhi-Hua Zhou (Nanjing University)

在强化学习中动态分配有限的内存资源
Dynamic allocation of limited memory resources in reinforcement learning
Nisheet Patel (University of Geneva) · Luigi Acerbi (University of Helsinki) · Alexandre Pouget (University of Geneva)

AttendLight:基于通用注意力的交通信号控制强化学习模型
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control
Afshin Oroojlooy (SAS Institute, Inc) · Mohammadreza Nazari (SAS Institute Inc.) · Davood Hajinezhad (SAS Institute Inc.) · Jorge Silva (SAS)

重要加权边界的得分函数梯度估计器的最优方差控制
Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds
Valentin Liévin (Technical University of Denmark) · Andrea Dittadi (Technical University of Denmark) · Anders Christensen (Technical University of Denmark) · Ole Winther (DTU and KU)

形态系统中探索性搜索的分层组织潜在模块
Hierarchically-Organized Latent Modules for Exploratory Search in Morphogenetic Systems
Mayalen Etcheverry (INRIA) · Clément Moulin-Frier (Inria) · Pierre-Yves Oudeyer (INRIA)

H-Mem:利用Hebbian Memory Networks发挥突触可塑性
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
Thomas Limbacher (Graz University of Technology) · Robert Legenstein (Graz University of Technology)

图策略网络,用于图的可转移主动学习
Graph Policy Network for Transferable Active Learning on Graphs
Shengding Hu (Tsinghua University) · Zheng Xiong (Tsinghua University / University of Oxford) · Meng Qu (Mila) · Xingdi Yuan (Microsoft Research) · Marc-Alexandre Côté (Microsoft Research) · Zhiyuan Liu (Tsinghua University) · Jian Tang (Mila)

您所有的损失都属于贝叶斯
All your loss are belong to Bayes
Christian Walder (DATA61) · Richard Nock (Data61, the Australian National University and the University of Sydney)

通过最佳运输进行模型融合
Model Fusion via Optimal Transport
Sidak Pal Singh (EPFL) · Martin Jaggi (EPFL)

通过重新连接进行训练:权重的位置与它们的值分离
Train by Reconnect: Decoupling Locations of Weights from their Values
Yushi Qiu (The University of Tokyo) · Reiji Suda (University of Tokyo)

用高斯过程因子模型识别神经种群活动的信号和噪声结构
Identifying the signal and noise structure underlying neural population activity with Gaussian process factor models
Stephen Keeley (Princeton University) · Mikio Aoi (Princeton University) · Yiyi Yu (UNC) · Spencer Smith (UC Santa Barbara) · Jonathan W Pillow (Princeton University)

DiffGCN:通过微分算子和代数多重网格池的图卷积网络
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
Moshe Eliasof (Ben-Gurion University of the Negev) · Eran Treister (Ben-Gurion University of the Negev)

非凸稀疏约束优化的可行水平近点法
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization
Digvijay Boob (Georgia Institute of Technology) · Qi Deng (Shanghai University of Finance and Economics) · Guanghui Lan (Georgia Tech) · Yilin Wang (Shanghai University of Finance and Economics)

忘记LiDAR:具有MED概率量的自我监督深度估计器
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes
Juan Luis Gonzalez (KAIST-VICLab) · Munchurl Kim (KAIST-VICLab)

深度统计求解器
Deep Statistical Solvers
Balthazar Donon (RTE R&D / Université Paris-Saclay) · Zhengying Liu (Inria/U. Paris-Sud) · Wenzhuo LIU (Inria Paris Saclay) · Isabelle Guyon (U. Paris-Saclay & ChaLearn) · Antoine Marot (RTE) · Marc Schoenauer (INRIA)

HRN:一种新的课堂学习方法
HRN: A New Approach to One Class Learning
Wenpeng Hu (Peking University) · Mengyu Wang (Peking University) · Qi Qin (Peking University) · Jinwen Ma (Peking University) · Bing Liu (Peking University)

弱形式广义哈密顿学习
Weak Form Generalized Hamiltonian Learning
Kevin L Course (University of Toronto) · Trefor Evans (University of Toronto) · Prasanth Nair (University of Toronto)

土匪学习算法及其在拍卖设计中的应用
A Bandit Learning Algorithm and Applications to Auction Design
Kim Thang Nguyen (IBISC, University Paris-Saclay)

学习具有自然权重的神经网络的难度
Hardness of Learning Neural Networks with Natural Weights
Amit Daniely (Hebrew University and Google Research) · Gal Vardi (Weizmann Institute of Science)

球面嵌入的深度度量学习
Deep Metric Learning with Spherical Embedding
Dingyi Zhang (Zhejiang University) · Yingming Li (Zhejiang University) · Zhongfei Zhang (Binghamton University)

使用多项式形式对离散动力系统中的不确定性进行推理。
Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms.
Sriram Sankaranarayanan (University of Colorado, Boulder) · Yi Chou (University of Colorado Boulder) · Eric Goubault (Ecole Polytechnique) · Sylvie Putot (Ecole Polytechnique)

神经元高斯过程回归
Neuronal Gaussian Process Regression
Johannes Friedrich (Flatiron Institute)

学习使用展开算法解决电视正则化问题
Learning to solve TV regularised problems with unrolled algorithms
Hamza Cherkaoui (CEA) · Jeremias Sulam (Johns Hopkins University) · Thomas Moreau (Inria)

训练噪声相互作用神经元的浅层和深层广义线性模型的新推理方法
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons
Gabriel Mahuas (ENS Paris-Saclay; IST Austria; LPENS) · Giulio Isacchini (Max Planck Institute for Dynamics and Selforganisation) · Olivier Marre (Institut de la vision) · Ulisse Ferrari (Universite Pier et Marie Curie) · Thierry Mora (ENS)

光谱时间图神经网络用于多元时间序列预测
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
Defu Cao (Peking University) · Yujing Wang (MSRA) · Juanyong Duan (Microsoft) · Ce Zhang (ETH Zurich) · Xia Zhu (Microsoft) · Congrui Huang (Microsoft) · Yunhai Tong (Peking University) · Bixiong Xu (Microsoft) · Jing Bai (Microsoft) · Jie Tong (Microsoft) · Qi Zhang (Microsoft)

HYDRA:修剪对抗性强壮的神经网络
HYDRA: Pruning Adversarially Robust Neural Networks
Vikash Sehwag (Princeton University) · Shiqi Wang (Columbia) · Prateek Mittal (Princeton University) · Suman Jana (Columbia University)

不完全POMDP的样本有效强化学习
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Chi Jin (Princeton University) · Sham Kakade (University of Washington & Microsoft Research) · Akshay Krishnamurthy (Microsoft) · Qinghua Liu (Princeton University)

带拒绝选项的回归并应用于kNN
Regression with reject option and application to kNN
Christophe Denis (Universite Paris Est) · Mohamed Hebiri (Université Gustave Eiffel) · Ahmed Zaoui (Université Gustave Eiffel)

曲率正则化以防止图形嵌入失真
Curvature Regularization to Prevent Distortion in Graph Embedding
Hongbin Pei (Jilin University) · Bingzhe Wei (University of Illinois at Urbana-Champaign) · Kevin Chang (University of Illinois at Urbana-Champaign) · Chunxu Zhang (Jilin University) · Bo Yang (Jilin University)

使用随机标签训练时,神经网络会学到什么?
What Do Neural Networks Learn When Trained With Random Labels?
Hartmut Maennel (Google) · Ibrahim Alabdulmohsin (Google Research) · Ilya Tolstikhin (Google, Brain Team, Zurich) · Robert Baldock (Google) · Olivier Bousquet (Google Brain (Zurich)) · Sylvain Gelly (Google Brain (Zurich)) · Daniel Keysers (Google Research, Brain Team)

在无限宽超网络上
On Infinite-Width Hypernetworks
Etai Littwin (Apple) · Tomer Galanti (Tel Aviv University) · Lior Wolf (Facebook AI Research) · Greg Yang (Microsoft Research)

通过归纳偏置从深度学习中发现符号模型
Discovering Symbolic Models from Deep Learning with Inductive Biases
Miles Cranmer (Princeton University) · Alvaro Sanchez Gonzalez (DeepMind) · Peter Battaglia (DeepMind) · Rui Xu (Princeton University) · Kyle Cranmer (New York University) · David Spergel (Flatiron Institute) · Shirley Ho (Flatiron institute)

一次几个因素的鲁棒解开
Robust Disentanglement of a Few Factors at a Time
Benjamin Estermann (ETH Zurich) · Markus Marks (ETH Zurich) · Mehmet Fatih Yanik (ETH Zürich)

基于全局低阶二阶模型的凸优化
Convex optimization based on global lower second-order models
Nikita Doikov (Catholic University of Louvain) · Yurii Nesterov (Catholic University of Louvain (UCL))

中场游戏的虚拟游戏:连续时间分析和应用
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
Sarah Perrin (Univ. Lille) · Julien Perolat (DeepMind) · Mathieu Lauriere (Princeton University) · Matthieu Geist (Google Brain) · Romuald Elie (Deepmind) · Olivier Pietquin (Google Research Brain Team)

Schatten-p拟范数的一种新型变体形式
A novel variational form of the Schatten-p quasi-norm
Paris Giampouras (The Johns Hopkins University) · Rene Vidal (Johns Hopkins University, USA) · Athanasios Rontogiannis (National Observatory of Athens) · Benjamin Haeffele (Johns Hopkins University)

非凸优化剪裁算法的改进分析
Improved Analysis of Clipping Algorithms for Non-convex Optimization
Bohang Zhang (Peking University) · Jikai Jin (Peking University) · Cong Fang (Peking University) · Liwei Wang (Peking University)

学习非参数因果图的多项式时间算法
A polynomial-time algorithm for learning nonparametric causal graphs
Ming Gao (the University of Chicago) · Yi Ding (University of Chicago) · Bryon Aragam (University of Chicago)

Wasserstein重心的公平回归
Fair regression with Wasserstein barycenters
Evgenii Chzhen (Université Paris-Saclay) · Christophe Denis (Universite Paris Est) · Mohamed Hebiri (Université Gustave Eiffel) · Luca Oneto (University of Genoa) · Massimiliano Pontil (IIT)

在无限支持下学习离散分布
Learning discrete distributions with infinite support
Doron Cohen (Ben-Gurion University of the Negev) · Aryeh Kontorovich (Ben Gurion University) · Geoffrey Wolfer (Ben-Gurion University of the Negev)

多层次预算组合问题的课程学习
Curriculum learning for multilevel budgeted combinatorial problems
Adel Nabli (Université de Montréal) · Margarida Carvalho (Université de Montréal)

估计线性非高斯潜变量图的广义独立噪声条件
Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs
Feng Xie (Peking University) · Ruichu Cai (Guangdong University of Technology) · Biwei Huang (Carnegie Mellon University) · Clark Glymour (Carnegie Mellon University) · Zhifeng Hao (Guangdong University of Technology) · Kun Zhang (CMU)

将语用推理交流纳入新兴语言
Incorporating Pragmatic Reasoning Communication into Emergent Language
Yipeng Kang (Tsinghua University) · Tonghan Wang (Tsinghua University) · Gerard de Melo (Hasso Plattner Institute)

通过最佳传输的几何数据集距离
Geometric Dataset Distances via Optimal Transport
David Alvarez Melis (MIT) · Nicolo Fusi (Microsoft Research)

从k-DPP采样而不查看所有项目
Sampling from a k-DPP without looking at all items
Daniele Calandriello (LCSL IIT/MIT) · Michal Derezinski (UC Berkeley) · Michal Valko (DeepMind)

AI Feynman 2.0:利用图模块性的帕累托最优符号回归
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Silviu-Marian Udrescu (MIT) · Andrew Tan (Massachusetts Institute of Technology) · Jiahai Feng (MIT) · Orisvaldo Neto (MIT) · Tailin Wu (MIT) · Max Tegmark (MIT)

通过插件估算器进行公平回归并通过统计保证进行重新校准
Fair regression via plug-in estimator and recalibration with statistical guarantees
Evgenii Chzhen (Université Paris-Saclay) · Christophe Denis (Universite Paris Est) · Mohamed Hebiri (Université Gustave Eiffel) · Luca Oneto (University of Genoa) · Massimiliano Pontil (IIT)

PAC-贝叶斯对样本相关先验的学习界限
PAC-Bayes Learning Bounds for Sample-Dependent Priors
Pranjal Awasthi (Rutgers University/Google) · Satyen Kale (Google) · Stefani Karp (Google/CMU) · Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research)

通过经验集中实现清晰一致的收敛边界
Sharp uniform convergence bounds through empirical centralization
Cyrus Cousins (Brown University) · Matteo Riondato (Amherst College)

使用cINN进行网络到网络的翻译
Network-to-Network Translation with cINNs
Robin Rombach (Heidelberg University) · Patrick Esser (Heidelberg University) · Bjorn Ommer (Heidelberg University)

联合蒸馏中的联合学习中的鲁棒模型融合
Ensemble Distillation for Robust Model Fusion in Federated Learning
Tao Lin (EPFL) · Lingjing Kong (EPFL) · Sebastian U Stich (EPFL) · Martin Jaggi (EPFL)

自旋加权球形CNN
Spin-Weighted Spherical CNNs
Carlos Esteves (University of Pennsylvania) · Ameesh Makadia (Google Research) · Kostas Daniilidis (University of Pennsylvania)

SnapBoost:异构助推器
SnapBoost: A Heterogeneous Boosting Machine
Thomas Parnell (IBM Research) · Andreea Anghel (IBM Research) · Małgorzata Łazuka (ETH Zürich) · Nikolas Ioannou (IBM Research) · Sebastian Kurella (ETH Zürich) · Peshal Agarwal (ETH Zürich) · Nikolaos Papandreou (IBM Research Zurich) · Haralampos Pozidis (IBM Research)

VAEM:用于异构混合类型数据的深度生成模型
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Chao Ma (University of Cambridge) · Sebastian Tschiatschek (Microsoft Research) · Richard E Turner (University of Cambridge) · José Miguel Hernández-Lobato (University of Cambridge) · Cheng Zhang (Microsoft Research, Cambridge, UK)

条件矩模型的极小极大估计
Minimax Estimation of Conditional Moment Models
Nishanth Dikkala (MIT) · Greg Lewis (Microsoft Research) · Lester Mackey (Microsoft Research) · Vasilis Syrgkanis (Microsoft Research)

内核方法:有效处理数十亿个点
Kernel Methods Through the Roof: Handling Billions of Points Efficiently
Giacomo Meanti (Universita’ di Genova) · Luigi Carratino (University of Genoa) · Lorenzo Rosasco (University of Genova- MIT - IIT) · Alessandro Rudi (INRIA, Ecole Normale Superieure)

随机路径积分微分估计期望最大化算法
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm
Gersende Fort (CNRS) · Eric Moulines (Ecole Polytechnique) · Hoi-To Wai (The Chinese University of Hong Kong)

ColdGAN:采用谨慎的抽样策略来驯服语言GAN
ColdGANs: Taming Language GANs with Cautious Sampling Strategies
Thomas Scialom (reciTAL) · Paul-Alexis Dray (reciTAL) · Sylvain Lamprier (LIP6-UPMC) · Benjamin Piwowarski (LIP6, UPMC / CNRS, Paris, France) · Jacopo Staiano (reciTAL)

对数修剪就是您所需要的
Logarithmic Pruning is All You Need
Laurent Orseau (DeepMind) · Marcus Hutter (DeepMind) · Omar Rivasplata (DeepMind & UCL)

SurVAE流量:推测可以弥合VAE和流量之间的差距
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Didrik Nielsen (DTU Compute) · Priyank Jaini (University of Waterloo) · Emiel Hoogeboom (University of Amsterdam) · Ole Winther (DTU and KU) · Max Welling (University of Amsterdam / Qualcomm AI Research)

使用正特征的线性时间Sinkhorn发散
Linear Time Sinkhorn Divergences using Positive Features
Meyer Scetbon (CREST-ENSAE) · Marco Cuturi (Google Brain & CREST - ENSAE)

共同熵在因果推理中的应用
Applications of Common Entropy in Causal Inference
Murat Kocaoglu (IBM Research) · Sanjay Shakkottai (University of Texas at Austin) · Alexandros Dimakis (University of Texas, Austin) · Constantine Caramanis (UT Austin) · Sriram Vishwanath (University of Texas at Austin)

近邻随机梯度Langevin算法的原始对偶解释
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm
Adil SALIM (KAUST) · Peter Richtarik (KAUST)

表征最优混合策略:在哪里干预和观察到什么
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe
Sanghack Lee (Columbia University) · Elias Bareinboim (Columbia University)

可逆的高斯重新参数化:重新研究Gumbel-Softmax
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
Andres Potapczynski (Columbia University) · Gabriel Loaiza-Ganem (Layer 6 AI) · John Cunningham (University of Columbia)

层次结构的等变网络
Equivariant Networks for Hierarchical Structures
Renhao Wang (University of British Columbia) · Marjan Albooyeh (University of British Columbia) · Siamak Ravanbakhsh (McGill / MILA)

RSKDD-Net:基于随机样本的关键点检测器和描述符
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
Fan Lu (Tongji University) · Guang Chen (Tongji University) · Yinlong Liu (Technische Universität München) · Zhongnan Qu (ETH Zurich) · Alois Knoll (Robotics and Embedded Systems)

通过搜索和学习生成无监督文本
Unsupervised Text Generation by Search and Learning
Jingjing LI (The Chinese University of Hong Kong) · Zichao Li (Huawei Noah’s Ark Lab) · Lili Mou (University of Alberta) · Xin Jiang (Huawei Noah’s Ark Lab) · Michael Lyu (CUHK) · Irwin King (Chinese University of Hong Kong)

自动编码变体自动编码器
The Autoencoding Variational Autoencoder
Taylan Cemgil (DeepMind) · Sumedh Ghaisas (DeepMind) · Krishnamurthy Dvijotham (DeepMind) · Sven Gowal (DeepMind) · Pushmeet Kohli (DeepMind)

批量归一化偏向深度网络中身份功能的剩余块
Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks
Soham De (DeepMind) · Sam Smith (Google Brain)

信息不完整的多主体协作的联合策略搜索
Joint Policy Search for Multi-agent Collaboration with Incomplete Information
Yuandong Tian (Facebook AI Research) · Qucheng Gong (Facebook AI Research) · Yu Jiang (Facebook AI Research)

消除多模式分类器中的偏差:通过最大化函数熵进行正则化
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies
Itai Gat (Technion) · Idan Schwartz (Technion) · Alexander Schwing (University of Illinois at Urbana-Champaign) · Tamir Hazan (Technion)

神经执行引擎:学习执行子例程
Neural Execution Engines: Learning to Execute Subroutines
Yujun Yan (University of Michigan) · Kevin Swersky (Google) · Danai Koutra (U Michigan) · Parthasarathy Ranganathan (Google) · Milad Hashemi (Google)

使用局部因素动力学的反事实数据增强
Counterfactual Data Augmentation using Locally Factored Dynamics
Silviu Pitis (University of Toronto) · Elliot Creager (University of Toronto) · Animesh Garg (Univ. of Toronto, Vector Institute, Nvidia)

带纹理的3D网格的卷积生成
Convolutional Generation of Textured 3D Meshes
Dario Pavllo (ETH Zurich) · Graham Spinks (KU Leuven) · Thomas Hofmann (ETH Zurich) · Marie-Francine Moens (KU Leuven) · Aurelien Lucchi (ETH Zurich)

熵因果推论:可识别性和有限样本结果
Entropic Causal Inference: Identifiability and Finite Sample Results
Spencer Compton (MIT) · Murat Kocaoglu (IBM Research) · Kristjan Greenewald (IBM Research) · Dmitriy Katz (IBM Research)

通用隐写术和水印技术:旨在了解和利用深度隐藏
Universal Steganography and Watermarking: Towards Understanding and Utilizing Deep Hiding
Chaoning Zhang (KAIST) · Philipp Benz (KAIST) · Adil Karjauv (KAIST) · Geng Sun (KAIST) · In Kweon (KAIST)

缩小数字化鸿沟:PixelCNN作为单层流程
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
Didrik Nielsen (DTU Compute) · Ole Winther (DTU and KU)

最小分配假设下的最优私有中位数估计
Optimal Private Median Estimation under Minimal Distributional Assumptions
Christos Tzamos (UW-Madison) · Emmanouil-Vasileios Vlatakis-Gkaragkounis (Columbia University) · Ilias Zadik (NYU)

管道PSRO:在大型游戏中寻找近似纳什均衡的可扩展方法
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
Stephen Mcaleer (UC Irvine) · J.B. Lanier (University of California Irvine) · Roy Fox (UC Irvine) · Pierre Baldi (UC Irvine)

超越懒惰训练的过张量张量分解
Beyond Lazy Training for Over-parameterized Tensor Decomposition
Xiang Wang (Duke University) · Chenwei Wu (Duke University) · Jason Lee (Princeton University) · Tengyu Ma (Stanford University) · Rong Ge (Duke University)

有向图的高阶谱聚类
Higher-Order Spectral Clustering of Directed Graphs
Valdimar Steinar Ericsson Laenen (FiveAI) · He Sun (School of Informatics, The University of Edinburgh)

参与者批评模型中的R学习为顺序决策提供了生物学相关的机制
R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making
Sergey Shuvaev (Cold Spring Harbor Laboratory) · Sarah Starosta (Washington University in St. Louis) · Duda Kvitsiani (Aarhus University) · Adam Kepecs (Washington University in St. Louis) · Alexei Koulakov (Cold Spring Harbor Laboratory)

黑盒优化与本地生成代理
Black-Box Optimization with Local Generative Surrogates
Sergey Shirobokov (Imperial College London) · Vladislav Belavin (National Research University Higher School of Economics) · Michael Kagan (SLAC / Stanford) · Andrei Ustyuzhanin (National Research University Higher School of Economics) · Atilim Gunes Baydin (University of Oxford)

具有缺失计数的面板计数数据的功能EM算法
A Functional EM Algorithm for Panel Count Data with Missing Counts
Alexander Moreno (Georgia Institute of Technology) · Zhenke Wu (University of Michigan) · Jamie Roslyn Yap (University of Michigan) · Cho Lam (University of Utah) · David Wetter (University of Utah) · Inbal Nahum-Shani (University of Michigan) · Walter Dempsey (University of Michigan) · James M Rehg (Georgia Tech)

针对噪声标签对深层神经网络进行鲁棒训练的核心集
Coresets for Robust Training of Deep Neural Networks against Noisy Labels
Baharan Mirzasoleiman (Stanford University) · Kaidi Cao (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

通过稳健的位置估计获得可靠的图神经网络
Reliable Graph Neural Networks via Robust Location Estimation
Simon Geisler (Technical University of Munich) · Daniel Zügner (Technical University of Munich) · Stephan Günnemann (Technical University of Munich)

通过条件链映射对混合信号进行序列到多序列学习
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals
Jing Shi (Institute of Automation Chinese Academy of Sciences) · Xuankai Chang (Johns Hopkins University) · Pengcheng Guo (Northwestern Polytechnical University) · Shinji Watanabe (Johns Hopkins University) · Yusuke Fujita (Hitachi) · Jiaming Xu (Institute of Automation Chinese Academy of Sciences) · Bo Xu (Institute of Automation, Chinese Academy of Sciences) · Lei Xie (Northwestern Polytechnical University)

您的分类器可以秘密满足多源域适应
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation
Naveen Venkat (Indian Institute of Science) · Jogendra Nath Kundu (Indian Institute of Science) · Durgesh K. Singh (Indian Institute of Science) · Ambareesh Revanur (Indian Institute of Science) · Venkatesh Babu R (Indian institute of science)

利用在线多类分类中的替代差距
Exploiting the Surrogate Gap in Online Multiclass Classification
Dirk van der Hoeven (Leiden University)

解剖神经ODE
Dissecting Neural ODEs
Stefano Massaroli (The University of Tokyo) · Michael Poli (KAIST) · Jinkyoo Park (KAIST) · Atsushi Yamashita (The University of Tokyo) · edit Hajime Asama (The University of Tokyo)

用贝叶斯神经网络整合地球物理模型
Ensembling geophysical models with Bayesian Neural Networks
Ushnish Sengupta (University of Cambridge) · Matt Amos (Lancaster University) · Scott Hosking (British Antarctic Survey) · Carl Edward Rasmussen (University of Cambridge) · Matthew Juniper (University of Cambridge) · Paul Young (Lancaster University)

不确定环境的神经表示中的波动估计
Estimating Fluctuations in Neural Representations of Uncertain Environments
Sahand Farhoodi (Boston University) · Mark Plitt (Stanford University) · Lisa Giocomo (Stanford University) · Uri Eden (Boston University)

代表性空间中的新颖性搜索,可有效进行样本探索
Novelty Search in representational space for sample efficient exploration
Ruo Yu Tao (University of Alberta) · Vincent Francois-Lavet (McGill) · Joelle Pineau (McGill University)

随机二元网络的路径样本分析梯度估计
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks
Alexander Shekhovtsov (Czech Technical University in Prague, Czech Republic) · Viktor Yanush (Lomonosov Moscow State University) · Boris Flach (Czech Technical University in Prague)

惩罚兰格文动力学,对平滑和对数凹形目标的惩罚逐渐消失
Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets
Avetik Karagulyan (Center for Research in Economics and Statistics / ENSAE / IPP) · Arnak Dalalyan (ENSAE ParisTech)

Im Patient CapsAndRuns:无限池中的近似最佳算法配置
ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool
Gellert Weisz (Deepmind) · András György (DeepMind) · Wei-I Lin (UBC) · Devon Graham (University of British Columbia) · Kevin Leyton-Brown (University of British Columbia) · Csaba Szepesvari (DeepMind / University of Alberta) · Brendan Lucier (Microsoft Research)

使用立方持久性发现时变fMRI数据的拓扑
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
Bastian Rieck (ETH Zurich) · Tristan Yates (Yale University) · Christian Bock (ETH Zurich) · Karsten Borgwardt (ETH Zurich) · Guy Wolf (Université de Motréal; Mila) · Nicholas Turk-Browne (Yale University) · Smita Krishnaswamy (Yale University)

比较器自适应凸强盗
Comparator-Adaptive Convex Bandits
Dirk van der Hoeven (Leiden University) · Ashok Cutkosky (Google Research) · Haipeng Luo (University of Southern California)

超解算器:快速连续深度模型
Hypersolvers: Toward Fast Continuous-Depth Models
Michael Poli (KAIST) · Stefano Massaroli (The University of Tokyo) · Atsushi Yamashita (The University of Tokyo) · edit Hajime Asama (The University of Tokyo) · Jinkyoo Park (KAIST)

图神经网络的迭代深度图学习:更好且鲁棒的节点嵌入
Iterative Deep Graph Learning for Graph NeuralNetworks: Better and Robust Node Embeddings
Yu Chen (Facebook) · Lingfei Wu (IBM Research AI) · Mohammed Zaki (RPI)

什么地方出了问题以及什么时候出了错?\对于时间序列黑盒模型而言,实例化功能的重要性
What went wrong and when? \ Instance-wise feature importance for time-series black-box models
Sana Tonekaboni (University of Toronto Vector Institute) · Shalmali Joshi (Vector Institute) · Kieran Campbell (University of British Columbia) · David Duvenaud (University of Toronto) · Anna Goldenberg ()

STLnet:信号时间逻辑强制多元递归神经网络
STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks
Meiyi Ma (University of Virginia) · Ji Gao (University of Virginia) · Lu Feng (University of Virginia) · John A Stankovic (University of Virginia)

从不良MLE救援神经峰值训练模型
Rescuing neural spike train models from bad MLE
Diego M Arribas (Stony Brook University) · Yuan Zhao (Stony Brook University) · Il Memming Park (Stony Brook University)

具有预测奖励的多主体主动感知
Multi-agent active perception with prediction rewards
Mikko Lauri (University of Hamburg) · Frans Oliehoek (TU Delft)

具有不可知论噪声的半空间对抗鲁棒正确学习的复杂性
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise
Ilias Diakonikolas (UW Madison) · Daniel M. Kane (UCSD) · Pasin Manurangsi (Google)

RL Unplugged:离线强化学习基准集
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning
Ziyu Wang (Deepmind) · Caglar Gulcehre (Deepmind) · Alexander Novikov (DeepMind) · Thomas Paine (DeepMind) · Sergio Gómez (DeepMind) · Konrad Zolna (DeepMind) · Rishabh Agarwal (Google Research, Brain Team) · Josh Merel (DeepMind) · Daniel Mankowitz (DeepMind) · Cosmin Paduraru (DeepMind) · Gabriel Dulac-Arnold (Google Research) · Jerry Li (Google) · Mohammad Norouzi (Google Brain) · Matthew Hoffman (DeepMind) · Nicolas Heess (Google DeepMind) · Nando de Freitas (DeepMind)

嵌套黎曼流形的高维贝叶斯优化
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds
Noémie Jaquier (Karlsruhe Institute of Technology) · Leonel Rozo (Bosch Center for Artificial Intelligence)

用于分布增强学习的局部时差代码
A local temporal difference code for distributional reinforcement learning
Pablo Tano (University of Geneva) · Peter Dayan (Max Planck Institute for Biological Cybernetics) · Alexandre Pouget (University of Geneva)

Synbols:使用综合数据集探索学习算法
Synbols: Probing Learning Algorithms with Synthetic Datasets
Alexandre Lacoste (Element AI) · Pau Rodríguez López (CVC UAB) · Frederic Branchaud-Charron (Element AI) · Parmida Atighehchian (ElementAI) · Massimo Caccia (MILA) · Issam Hadj Laradji (University of British Columbia) · Alexandre Drouin (Element AI) · Matthew Craddock (Element AI) · Laurent Charlin (MILA / U.Montreal) · David Vázquez (Element AI)

学习以最佳应对策略迭代发挥无新闻外交
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Thomas Anthony (DeepMind) · Tom Eccles (DeepMind) · Andrea Tacchetti (DeepMind) · János Kramár (DeepMind) · Ian Gemp (DeepMind) · Thomas Hudson (DeepMind) · Nicolas Porcel (DeepMind) · Marc Lanctot (DeepMind) · Julien Perolat (DeepMind) · Richard Everett (DeepMind) · Satinder Singh (DeepMind) · Thore Graepel (DeepMind) · Yoram Bachrach ()

信用分配中的前瞻性和后见性
Forethought and Hindsight in Credit Assignment
Veronica Chelu (McGill University) · Doina Precup (McGill University / Mila / DeepMind Montreal) · Hado van Hasselt (DeepMind)

当对口遇见中国民间旋律
When Counterpoint Meets Chinese Folk Melodies
Nan Jiang (Tsinghua University) · Sheng Jin (Tsinghua University) · Zhiyao Duan (Unversity of Rochester) · Changshui Zhang (Tsinghua University)

逃避Softmax的引力
Escaping the Gravitational Pull of Softmax
Jincheng Mei (University of Alberta / Google Brain) · Chenjun Xiao (University of Alberta) · Bo Dai (Google Brain) · Lihong Li (Google Research) · Csaba Szepesvari (DeepMind / University of Alberta) · Dale Schuurmans (Google Brain & University of Alberta)

具有上下文对象分割潜在空间的多样图像字幕
Diverse Image Captioning with Context-Object Split Latent Spaces
Shweta Mahajan (TU Darmstadt) · Stefan Roth (TU Darmstadt)

通过任务计算组织循环网络动态,以实现持续学习
Organizing recurrent network dynamics by task-computation to enable continual learning
Lea Duncker (Gatsby Unit, UCL) · Laura N Driscoll (Stanford) · Krishna V Shenoy (Stanford University) · Maneesh Sahani (Gatsby Unit, UCL) · David Sussillo (Stanford University)

凸松弛壁垒,再谈:加强的单神经元松弛神经网络​​验证
The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification
Christian Tjandraatmadja (Google) · Ross Anderson (Google Research) · Joey Huchette (Rice University) · Will Ma (Columbia University) · KRUNAL KISHOR PATEL (Google) · Juan Pablo Vielma (Google and MIT)

平滑和强凸分散优化的最佳实用算法
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
Dmitry Koralev (KAUST) · Adil SALIM (KAUST) · Peter Richtarik (KAUST)

量化变分推理
Quantized Variational Inference
Amir Dib (ENS Paris-Saclay, Université Paris-Saclay)

基于模型的强化学习的价值等价原理
The Value Equivalence Principle for Model-Based Reinforcement Learning
Christopher Grimm (University of Michigan) · Andre Barreto (DeepMind) · Satinder Singh (DeepMind) · David Silver (DeepMind)

Deep Graph Pose:一种半监督式深度图形模型,用于改善动物的姿势跟踪
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
Anqi Wu () · E. Kelly Buchanan (Columbia University) · Matthew Whiteway (Columbia University) · Michael Schartner (University of Geneva) · Guido Meijer (Champalimaud Center for the Unknown) · Jean-Paul Noel (New York University) · Erica Rodriguez (Columbia University) · Claire Everett (Columbia University) · Amy Norovich (Columbia University) · Evan Schaffer (Columbia University) · Neeli Mishra (Columbia University) · C. Daniel Salzman (Columbia University) · Dora Angelaki (New York University) · Andrés Bendesky (Columbia University) · The International Brain Laboratory The International Brain Laboratory (The International Brain Laboratory) · John Cunningham (University of Columbia) · Liam Paninski (Columbia University)

UCLID-Net:对象空间中的单视图重构
UCLID-Net: Single View Reconstruction in Object Space
Benoit Guillard (EPFL) · Edoardo Remelli (EPFL) · Pascal Fua (EPFL, Switzerland)

使用混合物的混合物进行无监督的声音分离
Unsupervised Sound Separation Using Mixtures of Mixtures
Scott Wisdom (Google) · Efthymios Tzinis (University of Illinois at Urbana-Champaign) · Hakan Erdogan (Google) · Ron Weiss (Google) · Kevin Wilson (Google) · John Hershey (Google)

利用弱监督的视觉模式从部分注释中学习
Exploiting weakly supervised visual patterns to learn from partial annotations
Kaustav Kundu (Amazon) · Joseph Tighe (Amazon)

高效的基于半定程序的二进制和多类MRF推理
Efficient semidefinite-programming-based inference for binary and multi-class MRFs
Chirag Pabbaraju (Carnegie Mellon University) · Po-Wei Wang (CMU) · J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

用变压器测量神经证明生成中的系统概括
Measuring Systematic Generalization in Neural Proof Generation with Transformers
Nicolas Gontier (Mila, Polytechnique Montréal) · Koustuv Sinha (McGill University / Mila / FAIR) · Siva Reddy (McGill University) · Chris Pal (Montreal Institute for Learning Algorithms, École Polytechnique, Université de Montréal)

深度学习中的方向性收敛和对齐
Directional convergence and alignment in deep learning
Ziwei Ji (University of Illinois Urbana-Champaign) · Matus Telgarsky (UIUC)

通过生物多样性优化进行精细的基因组重建
Finer Metagenomic Reconstruction via Biodiversity Optimization
Simon Foucart (Texas A&M) · David Koslicki (Pennsylvania State University)

单调算子平衡网络
Monotone operator equilibrium networks
Ezra Winston (Carnegie Mellon University) · J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

GramGAN:从2D示例中进行深3D纹理合成
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
Tiziano Portenier (ETH Zurich) · Siavash Arjomand Bigdeli (CSEM) · Orcun Goksel (ETH Zurich)

贝叶斯学习的分散Langevin动力学
Decentralized Langevin Dynamics for Bayesian Learning
Anjaly Parayil (Postdoctoral Associate, Army Research Laboratory ) · He Bai (Oklahoma State University) · Jemin George (Army Research Laboratory) · Prudhvi Gurram (Booz Allen Hamilton)

利用Jensen-Shannon-Divergence进行多模式生成学习
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Thomas M Sutter (ETH Zurich) · Imant Daunhawer (ETH Zurich) · Julia Vogt (ETH Zurich)

线性动力系统作为核心计算基元
Linear Dynamical Systems as a Core Computational Primitive
Shiva Kaul (Carnegie Mellon University)

通过考虑将来的任务来避免副作用
Avoiding Side Effects By Considering Future Tasks
Victoria Krakovna (DeepMind) · Laurent Orseau (DeepMind) · Richard Ngo (DeepMind) · Miljan Martic (DeepMind) · Shane Legg (DeepMind)

具有模糊查询注意的多主体轨迹预测
Multi-agent Trajectory Prediction with Fuzzy Query Attention
Nitin Kamra (University of Southern California) · Hao Zhu (Peking University) · Dweep Kumarbhai Trivedi (University of Southern California) · Ming Zhang (Peking University) · Yan Liu (University of Southern California)

过度参数化的对抗训练:克服维度诅咒的分析
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality
Yi Zhang (Princeton University) · Orestis Plevrakis (Princeton University) · Simon Du (Institute for Advanced Study) · Xingguo Li (Princeton University) · Zhao Song (IAS/Princeton) · Sanjeev Arora (Princeton University)

有信心时信任模型:基于蒙版模型的演员批评
Trust the Model When It Is Confident: Masked Model-based Actor-Critic
Feiyang Pan (Institute of Computing Technology, Chinese Academy of Sciences) · Jia He (Huawei) · Dandan Tu (Huawei) · Qing He (Institute of Computing Technology, Chinese Academy of Sciences)

连续时间和离散空间中的POMDP
POMDPs in Continuous Time and Discrete Spaces
Bastian Alt (Technische Universität Darmstadt) · Matthias Schultheis (Technische Universität Darmstadt) · Heinz Koeppl (Technische Universität Darmstadt)

使用条件生成模型的离散结构的目标定向生成
Goal-directed Generation of Discrete Structures with Conditional Generative Models
Maolaaisha Aminanmu (University of Geneva,University of Applied Sciences Western Switzerland) · Brooks Paige (University College London) · Alexandros Kalousis (University of Applied Sciences, Western Switzerland)

行列式点过程的在线MAP推断
Online MAP Inference of Determinantal Point Processes
Aditya Bhaskara (University of Utah) · Amin Karbasi (Yale) · Silvio Lattanzi (Google Research) · Morteza Zadimoghaddam (Google Research)

情景强化学习的稳态分析
Steady State Analysis of Episodic Reinforcement Learning
Huang Bojun (Rakuten Institute of Technology)

学习特征稀疏主子空间
Learning Feature Sparse Principal Subspace
Lai Tian (Northwestern Polytechnical University) · Feiping Nie (University of Texas Arlington) · Rong Wang (Northwestern Polytechnical University) · Xuelong Li (Northwestern Polytechnical Univ.)

通过结构化的专心推理学习多智能体交流
Learning Multi-Agent Communication through Structured Attentive Reasoning
Murtaza Rangwala (Virginia Tech) · Ryan K Williams (Virginia Tech)

早期学习正则化可防止记忆嘈杂的标签
Early-Learning Regularization Prevents Memorization of Noisy Labels
Sheng Liu (NYU) · Jonathan Niles-Weed (NYU) · Narges Razavian (New York University School of Medicine) · Carlos Fernandez-Granda (NYU)

因果Shapley值:利用因果知识来解释复杂模型的个体预测
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Tom Heskes (Radboud University Nijmegen) · Evi Sijben (Radboud University) · Ioan Gabriel Bucur (Radboud University Nijmegen) · Tom Claassen (Radboud University Nijmegen)

元强化学习的信息理论任务选择
Information-theoretic Task Selection for Meta-Reinforcement Learning
Ricardo Luna Gutierrez (University of Leeds) · Matteo Leonetti (University of Leeds)

自适应学习布隆过滤器(Ada-BF):分类器的有效利用
Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier
Zhenwei Dai (Rice University) · Anshumali Shrivastava (Rice University)

标准化卡尔曼滤波器以进行多元时间序列分析
Normalizing Kalman Filters for Multivariate Time Series Analysis
Emmanuel de Bézenac (Sorbonne Université) · Syama Sundar Rangapuram (Amazon Research) · Konstantinos Benidis (Amazon Research) · Michael Bohlke-Schneider (Amazon) · Lorenzo Stella (Amazon Research) · Hilaf Hasson (Amazon Research) · Richard Kurle (Volkswagen Group) · Tim Januschowski (Amazon Research) · Patrick Gallinari (Sorbonne University & Criteo AI Lab, Paris)

有向DAG的主动结构学习通过定向集团树
Active Structure Learning of Causal DAGs via Directed Clique Trees
Chandler Squires (Massachusetts Institute of Technology) · Sara Magliacane (MIT-IBM Watson AI Lab, IBM Research) · Kristjan Greenewald (IBM Research) · Dmitriy Katz (IBM Research) · Murat Kocaoglu (IBM Research) · Karthikeyan Shanmugam (IBM Research, NY)

EcoLight:在极端预算和网络约束下发展中地区的交叉口控制
EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints
Sachin Chauhan (IIT-Delhi) · Kashish Bansal (IIT Delhi) · Rijurekha Sen (IIT DELHI)

崩溃的土匪及其在公共卫生干预中的应用
Collapsing Bandits and Their Application to Public Health Intervention
Aditya Mate (Harvard University) · Jackson Killian (Harvard University) · Haifeng Xu (University of Virginia) · Andrew Perrault (Harvard University) · Milind Tambe (Harvard University/Google)

用于复杂目标和约束的一致插件分类器
Consistent Plug-in Classifiers for Complex Objectives and Constraints
Shiv Kumar Tavker (IIT Madras) · Harish Guruprasad Ramaswamy (IIT Madras) · Harikrishna Narasimhan (Google Research)

学习最佳手臂的最佳消除算法
An Optimal Elimination Algorithm for Learning a Best Arm
Avinatan Hassidim (Google) · Ron Kupfer (The Hebrew University of Jerusalem) · Yaron Singer (Harvard University)

Delta-STN:使用结构化响应雅可比矩阵的神经网络的高效双层优化
Delta-STN: Efficient Bilevel Optimization of Neural Networks using Structured Response Jacobians
Juhan Bae (University of Toronto) · Roger Grosse (University of Toronto)

傅立叶特征使网络可以在低维域中学习高频功能
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Matthew Tancik (UC Berkeley) · Pratul Srinivasan (UC Berkeley) · Ben Mildenhall (UC Berkeley) · Sara Fridovich-Keil (UC Berkeley) · Nithin Raghavan (UC Berkeley) · Utkarsh Singhal (UC Berkeley) · Ravi Ramamoorthi (University of California San Diego) · Jonathan Barron (Google Research) · Ren Ng (University of California, Berkeley)

一种简单有效的加速优化和局部探查平滑方法
A Simple and Efficient Smoothing Method for Accelerated Optimization and Local Exploration
Kevin Scaman (Noah’s Ark Lab, Huawei Technologies) · Ludovic DOS SANTOS (Huawei) · Merwan Barlier (Huawei Technologies) · Igor Colin (Huawei)

具有上下文信息的无监督顺序选择的在线算法
Online Algorithm for Unsupervised Sequential Selection with Contextual Information
Arun Verma (Indian Institute of Technology Bombay) · Manjesh Kumar Hanawal (IIT Bombay) · Csaba Szepesvari (DeepMind / University of Alberta) · Venkatesh Saligrama (Boston University)

使用焦点损失校准深层神经网络
Calibrating Deep Neural Networks using Focal Loss
Jishnu Mukhoti (University of Oxford) · Viveka Kulharia (University of Oxford) · Amartya Sanyal (University of Oxford) · Stuart Golodetz (FiveAI Ltd.) · Philip Torr (University of Oxford) · Puneet Dokania (University of Oxford)

收缩式模块化元学习
Modular Meta-Learning with Shrinkage
Yutian Chen (DeepMind) · Abram Friesen (DeepMind) · Feryal Behbahani (DeepMind) · Arnaud Doucet (Google DeepMind) · David Budden (DeepMind) · Matthew Hoffman (DeepMind) · Nando de Freitas (DeepMind)

ScaleCom:可扩展的稀疏梯度压缩,用于高效通信的分布式培训
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Chia-Yu Chen (IBM research) · Jiamin Ni (IBM) · Songtao Lu (IBM) · Xiaodong Cui (IBM T. J. Watson Research Center) · Pin-Yu Chen (IBM Research AI) · Xiao Sun (IBM Thomas J. Watson Research Center) · Naigang Wang (IBM T. J. Watson Research Center) · Swagath Venkataramani (IBM Research) · Vijayalakshmi (Viji) Srinivasan (IBM TJ Watson) · Wei Zhang (IBM T.J.Watson Research Center) · Kailash Gopalakrishnan (IBM Research)

走向实用的差分私人因果图发现
Towards practical differentially private causal graph discovery
Lun Wang (University of California, Berkeley) · Qi Pang (The Hong Kong University of Science and Technology) · Dawn Song (UC Berkeley)

通过概率比率限幅和样本加权增加GAN训练
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
Yue Wu (Carnegie Mellon University) · Pan Zhou (National University of Singapore) · Andrew Gordon Wilson (New York University) · Eric Xing (Petuum Inc. / Carnegie Mellon University) · Zhiting Hu (Carnegie Mellon University)

Top-KAST:Top-K Always稀疏训练
Top-KAST: Top-K Always Sparse Training
Siddhant Jayakumar (Google DeepMind) · Razvan Pascanu (Google DeepMind) · Jack Rae (DeepMind, UCL) · Simon Osindero (DeepMind) · Erich Elsen (DeepMind)

上下文随机强盗问题中的模型选择
Model Selection in Contextual Stochastic Bandit Problems
Aldo Pacchiano (UC Berkeley) · My Phan (University of Massachusetts Amherst) · Yasin Abbasi Yadkori (VinAI Research/ VinTech JSC.,) · Anup Rao (School of Computer Science, Georgia Tech) · Julian Zimmert (Google) · Tor Lattimore (DeepMind) · Csaba Szepesvari (DeepMind / University of Alberta)

从配对比较中同时进行偏好和度量学习
Simultaneous Preference and Metric Learning from Paired Comparisons
Austin Xu (Georgia Institute of Technology) · Mark Davenport (Georgia Institute of Technology)

用于新视图合成的无监督连续对象表示网络
Unsupervised Continuous Object Representation Networks for Novel View Synthesis
Nicolai Hani (University of Minnesota) · Selim Engin (University of Minnesota) · Jun-Jee Chao (University of Minnesota) · Volkan Isler (University of Minnesota, Twin Cities)

在医学图像分割中将人为错误与真实性区分开来
Disentangling Human Error from Ground Truth in Segmentation of Medical Images
Le Zhang (University College London) · Ryutaro Tanno (Microsoft Research / UCL) · Moucheng Xu (University College London) · Chen Jin (University College London) · Joseph Jacob (University College London) · Olga Cicarrelli (Queen Square Multiple Sclerosis Centre) · Frederik Barkhof (University College London) · Daniel Alexander (University College London)

通过实例感知参数化减轻在线继续学习中的遗忘
Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization
Hung-Jen Chen (National Tsing Hua University) · An-Chieh Cheng (National Tsing Hua University) · Da-Cheng Juan (Google) · Wei Wei (CMU) · Min Sun (Appier, Inc.)

从动物决策中推断学习规则
Inferring learning rules from animal decision-making
Zoe Ashwood (Princeton University) · Nicholas A Roy (Princeton Neuroscience Institute) · Ji Hyun Bak (UC Berkeley) · Jonathan W Pillow (Princeton University)

学习快速求解的微分方程
Learning Differential Equations that are Fast to Solve
Jacob Kelly (University of Toronto) · Jesse Bettencourt (University of Toronto) · Matthew Johnson (Google Brain) · David Duvenaud (University of Toronto)

卷积张量训练LSTM用于时空学习
Convolutional Tensor-Train LSTM for Spatio-Temporal Learning
Jiahao Su (University of Maryland) · Wonmin Byeon (NVIDIA Research) · Jean Kossaifi (NVIDIA) · Furong Huang (University of Maryland) · Jan Kautz (NVIDIA) · Anima Anandkumar (NVIDIA / Caltech)

双重Q学习的均方误差
The Mean-Squared Error of Double Q-Learning
Wentao Weng (Tsinghua University) · Harsh Gupta (University of Illinois at Urbana-Champaign) · Niao He (UIUC) · Lei Ying (University of Michigan) · R. Srikant (University of Illinois at Urbana-Champaign)

内隐在线学习中的时间变异性
Temporal Variability in Implicit Online Learning
Nicolò Campolongo (Università degli Studi di Milano) · Francesco Orabona (Boston University)

GAN的实例选择
Instance Selection for GANs
Terrance DeVries (University of Guelph) · Michal Drozdzal (FAIR) · Graham W Taylor (University of Guelph)

联合约束的经验过程方法:组合和线性强盗的实用算法
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits
Julian Katz-Samuels () · Lalit Jain (University of Washington) · zohar karnin (Amazon) · Kevin Jamieson (U Washington)

fMRI数据的神经地形因素分析
Neural Topographic Factor Analysis for fMRI Data
Eli Sennesh (Northeastern University) · Zulqarnain Khan (Northeastern University) · Yiyu Wang (Northeastern University) · J Benjamin Hutchinson (University of Oregon) · Ajay Satpute (Northeastern) · Jennifer Dy (Northeastern University) · Jan-Willem van de Meent (Northeastern University)

样本复杂度和有效维数,用于在流形上进行回归
Sample complexity and effective dimension for regression on manifolds
Andrew McRae (Georgia Institute of Technology) · Mark Davenport (Georgia Institute of Technology) · Justin Romberg (Georgia Institute of Technology)

代数约束的概率推论:理论极限和实际近似
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations
Zhe Zeng (University of California, Los Angeles) · Paolo Morettin (University of Trento) · Fanqi Yan (University of California, Los Angeles) · Antonio Vergari (University of California, Los Angeles) · Guy Van den Broeck (UCLA)

具有因果驱动的嵌入的成分识别
Compositional recognition with causally-driven embeddings
Yuval Atzmon (NVIDIA Research) · Felix Kreuk (Bar-Ilan University) · Uri Shalit (Technion) · Gal Chechik (NVIDIA, BIU)

学习离散分布:用户vs项目级隐私
Learning discrete distributions: user vs item-level privacy
Yuhan Liu (Cornell University) · Ananda Theertha Suresh (Google) · Felix Xinnan Yu (Google Research) · Sanjiv Kumar (Google Research) · Michael D Riley (Google)

在Ising模型中测试结构更改的限制
Limits on Testing Structural Changes in Ising Models
Aditya Gangrade (Boston University) · Bobak Nazer (Boston University) · Venkatesh Saligrama (Boston University)

几乎肯定稳定的深度动力学
Almost Surely Stable Deep Dynamics
Nathan Lawrence (University of British Columbia) · Philip Loewen (University of British Columbia) · Michael Forbes (Honeywell) · Johan Backstrom (Honeywell) · Bhushan Gopaluni (University of British Columbia)

从头开始学习卷积
Towards Learning Convolutions from Scratch
Behnam Neyshabur (Google)

贝叶斯位:统一量化和修剪
Bayesian Bits: Unifying Quantization and Pruning
Mart van Baalen (Qualcomm) · Christos Louizos (Qualcomm AI Research) · Markus Nagel (Qualcomm) · Rana Ali Amjad (Qualcomm) · Ying Wang (Qualcomm) · Tijmen Blankevoort (Qualcomm) · Max Welling (University of Amsterdam / Qualcomm AI Research)

用于通用纹理图像合成的神经FFT
Neural FFTs for Universal Texture Image Synthesis
Morteza Mardani (NVIDIA) · Guilin Liu (NVIDIA) · Aysegul Dundar (NVIDIA) · Shiqiu Liu (NVIDIA) · Andrew Tao (Nvidia Corporation) · Bryan Catanzaro (NVIDIA)

面向任务的对话的简单语言模型
A Simple Language Model for Task-Oriented Dialogue
Ehsan Hosseini-Asl (Salesforce Research) · Bryan McCann (Salesforce Research) · Chien-Sheng Wu (Salesforce Research) · Semih Yavuz (Salesforce) · Richard Socher (Salesforce)

神经网络的早期学习动力学的惊人的简单性
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks
Wei Hu (Princeton University) · Lechao Xiao (Google Brain) · Ben Adlam (Google) · Jeffrey Pennington (Google Brain)

内生采样的经验收益最大化算法的博弈分析
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
Xiaotie Deng (Peking University) · Ron Lavi (Technion) · Tao Lin (Peking University) · Qi Qi (Hong Kong University of Science and Technology) · Wenwei WANG (Alibaba Group) · Xiang Yan (Shanghai Jiao Tong University)

通过凸优化了解尖峰网络
Understanding spiking networks through convex optimization
Allan Mancoo (Champalimaud Centre for the Unknown) · Sander Keemink (Champalimaud Centre for the Unknown) · Christian K Machens (Champalimaud Centre for the Unknown)

预测的Stein变分梯度下降
Projected Stein Variational Gradient Descent
Peng Chen (The University of Texas at Austin) · Omar Ghattas (The University of Texas at Austin)

PEP:通过扰动进行参数合并
PEP: Parameter Ensembling by Perturbation
Alireza Mehrtash (University of British Columbia) · Purang Abolmaesumi (UBC) · Polina Golland (Massachusetts Institute of Technology) · Tina Kapur (Brigham and Women’s Hospital) · Demian Wassermann (Inria) · William Wells (Harvard Medical School)

微小的转移学习:迈向记忆有效的设备上学习
Tiny Transfer Learning: Towards Memory-Efficient On-Device Learning
Han Cai (Massachusetts Institute of Technology) · Chuang Gan (MIT-IBM Watson AI Lab) · Ligeng Zhu (MIT) · Song Han (MIT)

SVGD作为卡方散度的核化梯度流
SVGD as a kernelized gradient flow of the chi-squared divergence
Sinho Chewi (Massachusetts Institute of Technology) · Thibaut Le Gouic (Massachusetts Institute of Technology) · Chen Lu (Massachusetts Institute of Technology) · Tyler Maunu (Massachusetts Institute of Technology) · Philippe Rigollet (MIT)

通用计算图的可证明,可扩展和自动扰动分析
Provable, Scalable and Automatic Perturbation Analysis on General Computational Graphs
Kaidi Xu (Northeastern University) · Zhouxing Shi (UCLA) · Huan Zhang (UCLA) · Yihan Wang (JD.com) · Kai-Wei Chang (UCLA) · Minlie Huang (Tsinghua University) · Bhavya Kailkhura (Lawrence Livermore National Lab) · Xue Lin (Northeastern University) · Cho-Jui Hsieh (UCLA)

学习总结人类反馈
Learning to summarize with human feedback
Nisan Stiennon (OpenAI) · Long Ouyang (OpenAI) · Jeffrey Wu (OpenAI) · Daniel Ziegler (OpenAI) · Ryan Lowe (McGill University / OpenAI) · Chelsea Voss (OpenAI) · Alec Radford (OpenAI) · Dario Amodei (OpenAI) · Paul Christiano (OpenAI)

可检测到的分布最差数据的最坏情况保证
Provable Worst Case Guarantees for the Detection of Out-of-distribution Data
Julian Bitterwolf (University of Tübingen) · Alexander Meinke (University of Tübingen) · Matthias Hein (University of Tübingen)

插值技术可加快神经常微分方程中的梯度传播
Interpolation technique to speed up gradients propagation in Neural Ordinary Differential Equations
Talgat Daulbaev (Skolkovo Institute of Science and Technology) · Alexandr Katrutsa (Skolkovo Institute of Science and Technology) · Larisa Markeeva (Skolkovo Institute of Science and Technology) · Julia Gusak (Skolkovo Institute of Science and Technology) · Andrzej Cichocki (Skolkovo Institute of Science and Technology) · Ivan Oseledets (Skoltech)

情景强化学习中的乐观主义统一观
A Unifying View of Optimism in Episodic Reinforcement Learning
Gergely Neu (Universitat Pompeu Fabra) · Ciara Pike-Burke (Imperial College London)

基于光滑Wasserstein距离的生成建模的渐近保证
Asymptotic Guarantees for Generative Modeling based on the Smooth Wasserstein Distance
Ziv Goldfeld (Cornell University) · Kristjan Greenewald (IBM Research) · Kengo Kato (Cornell University)

估计ROC曲线下的加权面积
Estimating weighted areas under the ROC curve
Andreas Maurer () · Massimiliano Pontil (IIT & UCL)

基于多个标准的偏好学习:博弈论的观点
Preference learning along multiple criteria: A game-theoretic perspective
Kush Bhatia (UC Berkeley) · Ashwin Pananjady (UC Berkeley) · Peter Bartlett (UC Berkeley) · Anca Dragan (UC Berkeley) · Martin Wainwright (UC Berkeley)

CompReSS:压缩表示形式的自主学习
CompReSS: Compressing Representations for Self-Supervised Learning
Soroush Abbasi Koohpayegani (University of Maryland Baltimore County) · Ajinkya Tejankar (University of Maryland Baltimore County) · Hamed Pirsiavash (University of Maryland, Baltimore County)

GAMA:引导式对抗保证金攻击
GAMA: Guided Adversarial Margin Attack
Gaurang Sriramanan (Indian Institute of Science, Bangalore) · Sravanti Addepalli (Indian Institute of Science) · Arya Baburaj (Indian Institute of Science) · Venkatesh Babu R (Indian institute of science)

自适应学习以快速适应
Adaptive Learning for Fast Adaptation
Sungyong Baik (Seoul National University) · Myungsub Choi (Seoul National University) · Janghoon Choi (Seoul National University) · Heewon Kim (Seoul National University) · Kyoung Mu Lee (Seoul National University)

通过GPU Atari仿真加速强化学习
Accelerating Reinforcement Learning through GPU Atari Emulation
Steven Dalton (Nvidia) · iuri frosio (nvidia)

SEVIR:用于雷达和卫星气象学中的深度学习应用的风暴事件图像数据集
SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology
Siddharth Samsi (MIT Lincoln Laboratory) · Mark Veillette (MIT Lincoln Laboratory) · Chris Mattioli (MIT Lincoln Laboratory)

使用语义感知探索的目标目标导航
Object Goal Navigation using Semantically Aware Exploration
Devendra Singh Chaplot (Carnegie Mellon University) · Dhiraj Prakashchand Gandhi (Carnegie Mellon University) · Abhinav Gupta (Facebook AI Research/CMU) · Russ Salakhutdinov (Carnegie Mellon University)

大概正确的约束学习
Probably Approximately Correct Constrained Learning
Luiz Chamon (University of Pennsylvania) · Alejandro Ribeiro (University of Pennsylvania)

动态在线学习:极简主义视角
Online learning with dynamics: A minimax perspective
Kush Bhatia (UC Berkeley) · Karthik Sridharan (Cornell University)

学习近似布雷格曼散度
Learning to Approximate a Bregman Divergence
Ali Siahkamari (Boston University) · XIDE XIA (Boston University) · Venkatesh Saligrama (Boston University) · David Castañón (Boston University) · Brian Kulis (Boston University and Amazon)

具有数据增强功能的深度子空间聚类
Deep Subspace Clustering with Data Augmentation
Mahdi Abavisani (Rutgers, The State University of New Jersey) · Alireza Naghizadeh (Rutgers University) · Dimitris Metaxas (Rutgers University) · Vishal Patel (Johns Hopkins University)

GAN的Top-k培训:通过扔掉不良样本来提高GAN性能
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
Samarth Sinha (University of Toronto, Vector Institute) · Zhengli Zhao (UCI, Google Brain) · Anirudh Goyal ALIAS PARTH GOYAL (Université de Montréal) · Colin A Raffel (Google Brain) · Augustus Odena (Google Brain)

高维神经穗序列中用于序列检测的点过程模型
Point process models for sequence detection in high-dimensional neural spike trains
Alex H Williams (Stanford University) · Anthony Degleris (Stanford University) · Yixin Wang (Columbia University) · Scott Linderman (Stanford University)

标签感知神经正切核:更好的泛化和局部弹性
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity
Shuxiao Chen (University of Pennsylvania) · Hangfeng He (University of Pennsylvania) · Weijie Su (The Wharton School, University of Pennsylvania)

使用线性和一隐藏层神经网络进行转移学习的Minimax下界
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
Mir Mohammadreza Mousavi Kalan (University of Southern California) · Zalan Fabian (University of Southern California) · Salman Avestimehr (University of Southern California) · Mahdi Soltanolkotabi (University of Southern california)

通过孩子的眼睛进行自我监督学习
Self-supervised learning through the eyes of a child
Emin Orhan (New York University) · Vaibhav Gupta (New York University) · Brenden Lake (New York University)

诺伊曼网络:用于缺少价值的监督学习的差分编程。
Neumann networks: differential programming for supervised learning with missing values.
Marine Le Morvan (INRIA) · Julie Josses (CMAP / CNRS) · Thomas Moreau (Inria) · Erwan Scornet (Ecole Polytechnique) · Gael Varoquaux (Parietal Team, INRIA)

深层证据回归
Deep Evidential Regression
Alexander Amini (MIT) · Wilko Schwarting (Massachusetts Institute of Technology) · Ava Soleimany (MIT) · Daniela Rus (Massachusetts Institute of Technology)

JAX MD:微分物理学框架
JAX MD: A Framework for Differentiable Physics
Samuel Schoenholz (Google Brain) · Ekin Dogus Cubuk (Google Brain)

wav2vec 2.0:语音表示自我监督学习的框架
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
Alexei Baevski (Facebook AI Research) · Yuhao Zhou (University of Toronto) · Abdel-rahman Mohamed (Facebook AI Research (FAIR)) · Michael Auli (Facebook AI Research)

通过随机审阅者分配减轻对等审阅中的操纵
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
Steven Jecmen (Carnegie Mellon University) · Hanrui Zhang (Duke University) · Ryan Liu (Carnegie Mellon University) · Nihar Shah (CMU) · Vincent Conitzer (Duke University) · Fei Fang (Carnegie Mellon University)

多元回归的凸优化公式
A convex optimization formulation for multivariate regression
Yunzhang Zhu (Ohio State University)

置信序列,无需替换即可采样
Confidence sequences for sampling without replacement
Ian Waudby-Smith (Carnegie Mellon University) · Aaditya Ramdas (CMU)

关于两个神经网络之间的距离和学习的稳定性
On the distance between two neural networks and the stability of learning
Jeremy Bernstein (Caltech) · Arash Vahdat (NVIDIA) · Yisong Yue (Caltech) · Ming-Yu Liu (Nvidia Research)

基于持久性景观的高效拓扑层
Efficient Topological Layer based on Persistent Landscapes
Kwangho Kim (Carnegie Mellon University) · Jisu Kim (Inria Saclay) · Manzil Zaheer (Google Research) · Joon Kim (Carnegie Mellon University) · Frederic Chazal (INRIA) · Larry Wasserman (Carnegie Mellon University)

自我注意深度效率的限制
Limits to Depth Efficiencies of Self-Attention
Yoav Levine (HUJI) · Noam Wies (Hebrew University of Jerusalem) · Or Sharir (Hebrew University of Jerusalem) · Hofit Bata (Hebrew University of Jerusalem) · Amnon Shashua (Hebrew University of Jerusalem)

连续时间事件数据的用户相关神经序列模型
User-Dependent Neural Sequence Models for Continuous-Time Event Data
Alex Boyd (UC Irvine) · Robert Bamler (University of California at Irvine) · Stephan Mandt (University of California, Irivine) · Padhraic Smyth (University of California, Irvine)

没有重新参数化技巧的离散变分递归主题模型
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick
Mohammad Mehdi Rezaee Taghiabadi (university of maryland baltimore county) · Francis Ferraro (University of Maryland Baltimore County)

高效成对序列比对的秩一模型的自适应学习
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment
Govinda Kamath (Microsoft Research) · Tavor Baharav (Stanford University) · Ilan Shomorony (University of Illinois at Urbana Champaign)

基于傅立叶变换的归因先验可提高基因组学深度学习模型的可解释性和稳定性
Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics
Alex Tseng (Stanford University) · Avanti Shrikumar (Stanford University) · Anshul Kundaje (Stanford University)

再探潜匪
Latent Bandits Revisited
Joey Hong (Google AI) · Manzil Zaheer (Google Research) · Yinlam Chow (Google Research) · Branislav Kveton (Google Research) · Amr Ahmed (Google Research) · Craig Boutilier (Google)

从图像进行预测和控制的无监督拉格朗日动力学学习
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control
Yaofeng Desmond Zhong (Princeton University) · Naomi Leonard (Princeton University)

可通过字典学习对结构化张量进行在线CP / PARAFAC分解
Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
Sirisha Rambhatla (University of Southern California) · Xingguo Li (Princeton University) · Jarvis Haupt (University of Minnesota)

针对状态观察的对抗性摄动的鲁棒深度强化学习
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
Huan Zhang (UCLA) · Hongge Chen (MIT) · Chaowei Xiao (University of Michigan, Ann Arbor) · Bo Li (UIUC) · mingyan liu (university of Michigan, Ann Arbor) · Duane Boning (Massachusetts Institute of Technology) · Cho-Jui Hsieh (UCLA)

递归量子神经网络
Recurrent Quantum Neural Networks
Johannes Bausch (University of Cambridge)

成对比较学习的公理
Axioms for Learning from Pairwise Comparisons
Ritesh Noothigattu (Carnegie Mellon University) · Dominik Peters (Carnegie Mellon University) · Ariel Procaccia (Harvard University)

大鸟:更长序列的伯特
Big Bird: Bert for Longer Sequences
Manzil Zaheer (Google Research) · Guru Guruganesh (Google Research) · Kumar Avinava Dubey (Carnegie Mellon University) · Joshua Ainslie (Google) · Chris Alberti (Google) · Santiago Ontanon (Google LLC) · Philip Pham (Google) · Anirudh Ravula (Google) · Qifan Wang (Google Research) · Li Yang (Google) · Amr Ahmed (Google Research)

迁移学习中正在迁移什么?
What is being transferred in transfer learning?
Behnam Neyshabur (Google) · Hanie Sedghi (Google Brain) · Chiyuan Zhang (Google Brain)

基于模型的深度强化学习在想象与现实之间架起桥梁
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning
Guangxiang Zhu (Tsinghua university) · Minghao Zhang (Tsinghua University) · Honglak Lee (Google / U. Michigan) · Chongjie Zhang (Tsinghua University)

黑盒分类器的生成因果解释
Generative causal explanations of black-box classifiers
Matthew O’Shaughnessy (Georgia Tech) · Gregory Canal (Georgia Institute of Technology) · Marissa Connor (Georgia Tech) · Christopher Rozell (Georgia Institute of Technology) · Mark Davenport (Georgia Institute of Technology)

Wasserstein近邻梯度算法
The Wasserstein Proximal Gradient Algorithm
Adil SALIM (KAUST) · Anna Korba (Gatsby Unit - UCL) · Giulia Luise (University College London)

神经过程的因式分解神经过程:神经反应的K-Shot预测
Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses
Ronald Cotton (Shirley Ryan AbilityLab) · Fabian Sinz (University Tübingen) · Andreas Tolias (Baylor College of Medicine)

揭开矛盾的自我监督学习的神秘面纱:不变性,扩充性和数据集偏差
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases
Senthil Purushwalkam Shiva Prakash (Carnegie Mellon University) · Abhinav Gupta (Facebook AI Research/CMU)

特征偏移检测:通过条件分布测试确定哪些特征偏移
Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests
Sean Kulinski (Purdue University) · Saurabh Bagchi (Purdue University) · David Inouye (Purdue University)

通过增强蒸馏的表格数据的快速,准确和简单模型
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Rasool Fakoor (Amazon AWS) · Jonas Mueller (Amazon Web Services) · Nick Erickson (Amazon Web Services) · Pratik Chaudhari (University of Pennsylvania) · Alexander Smola (Amazon - We are hiring!)

大模型对半监督学习的不合理有效性
The Unreasonable Effectiveness of Big Models for Semi-Supervised Learning
Ting Chen (Google) · Simon Kornblith (Google Brain) · Kevin Swersky (Google) · Mohammad Norouzi (Google Brain) · Geoffrey E Hinton (Google & University of Toronto)

神经元的成分解释
Compositional Explanations of Neurons
Jesse Mu (Stanford University) · Jacob Andreas (MIT)

端到端学习和游戏干预
End-to-End Learning and Intervention in Games
Jiayang Li (Northwestern University) · Jing Yu (Northwestern University) · Yu Nie (Northwestern University) · Zhaoran Wang (Northwestern University)

可行的追索权摘要:发现追索权的偏见和差异
Actionable Recourse Summaries: Uncovering Biases and Disparities in Recourse
Kaivalya Rawal (Harvard University) · Himabindu Lakkaraju (Harvard)

从不定期抽样的局部观测中学习连续系统动力学
Learning Continuous System Dynamics fromIrregularly-Sampled Partial Observations
Zijie Huang (University of California, Los Angeles) · Yizhou Sun (UCLA) · Wei Wang (UCLA)

通过离散兰格文MCMC的Rényi发散分析,更快地进行差分私人采样
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
Arun Ganesh (University of California Berkeley) · Kunal Talwar (Google)

直接策略梯度:离散操作空间中策略的直接优化
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Guy Lorberbom (Technion) · Chris J. Maddison (University of Toronto) · Nicolas Heess (Google DeepMind) · Tamir Hazan (Technion) · Daniel Tarlow (Google Brain)

哈密​​顿蒙特卡洛法,使用伴随微分的拉普拉斯近似
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation
Charles Margossian (Columbia) · Aki Vehtari (Aalto University) · Daniel Simpson (University of Toronto) · Raj Agrawal (MIT)

前瞻性元学习,持续学习
Look-ahead Meta Learning for Continual Learning
Gunshi Gupta (University of montreal) · Karmesh Yadav (Carnegie) · Liam Paull (Université de Montréal)

具有空间随机网络的低失真块重采样
Low Distortion Block-Resampling with Spatially Stochastic Networks
Martin Arjovsky (New York University) · Darryl Barnhart (Latent Space) · Ian Thompson (Latent Space) · Sarah Hong (Latent Space)

贪婪推断与结构开发的惰性映射
Greedy inference with structure-exploiting lazy maps
Michael C Brennan (Massachusetts Institute of Technology) · Daniele Bigoni (Massachusetts Institute of Technology) · Olivier Zahm (INRIA) · Alessio Spantini (Massachusetts Institute of Technology) · Youssef Marzouk (Massachusetts Institute of Technology)

评论家正则回归
Critic Regularized Regression
Ziyu Wang (Deepmind) · Alexander Novikov (DeepMind) · Konrad Zolna (DeepMind) · Josh Merel (DeepMind) · Jost Tobias Springenberg (DeepMind) · Scott Reed (Google DeepMind) · Bobak Shahriari (Deepmind) · Noah Siegel (DeepMind) · Caglar Gulcehre (DeepMind) · Nicolas Heess (Google DeepMind) · Nando de Freitas (DeepMind)

通过指纹生成语言对文本进行匿名处理
De-Anonymizing Text by Fingerprinting Language Generation
Zhen Sun (Cornell University) · Roei Schuster (Cornell Tech) · Vitaly Shmatikov (Cornell University)

统一交换系统的观点和Q学习算法的收敛性分析
A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms
Niao He (UIUC) · Donghwan Lee (KAIST)

基于模型的强化学习的自适应离散化
Adaptive Discretization for Model-Based Reinforcement Learning
Sean Sinclair (Cornell University) · Tianyu Wang (Duke University) · Gauri Jain (Cornell University) · Siddhartha Banerjee (Cornell University) · Christina Yu (Cornell University)

线性强盗中的最佳手臂识别
Optimal Best-arm Identification in Linear Bandits
Yassir Jedra (KTH) · Alexandre Proutiere (KTH)

通过乘性权重更新学习合成功能
Learning compositional functions via multiplicative weight updates
Jeremy Bernstein (Caltech) · Jiawei Zhao (Caltech) · Markus Meister (Caltech) · Ming-Yu Liu (NVIDIA) · Anima Anandkumar (NVIDIA / Caltech) · Yisong Yue (Caltech)

通过动态确定性马尔可夫决策过程进行后悔的有状态定价
Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes
Yuval Emek (Technion - Israel Institute of Technology) · Ron Lavi (Technion) · Rad Niazadeh (Chicago Booth School of Business) · Yangguang Shi (Technion - Israel Institute of Technology)

密集大型网络中的A / B测试:设计和推理
A/B Testing in Dense Large-Scale Networks: Design and Inference
Preetam Nandy (LinkedIn Corporation) · Kinjal Basu (LinkedIn) · Shaunak Chatterjee (Linkedin) · Ye Tu (LinkedIn Corporation)

大数据集上的贝叶斯学习的副本交换Nos’e-Hoover动力学
Replica-Exchange Nos'e-Hoover Dynamics for Bayesian Learning on Large Datasets
Rui Luo (University College London) · Qiang Zhang (University College London) · Yaodong Yang (University College London) · Jun Wang (JD AI Research & UCL)

可以很好地进行批量非政策强化学习,而无需进行大量探索
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration
Yao Liu (Stanford University) · Adith Swaminathan (Microsoft Research) · Alekh Agarwal (Microsoft Research) · Emma Brunskill (Stanford University)

深度潜伏的深层变形金刚
Deep Transformers with Latent Depth
Xian Li (Facebook) · Asa Cooper Stickland (University of Edinburgh) · Yuqing Tang (Facebook AI) · Xiang Kong (Carnegie Mellon University)

流图的自适应收缩估计
Adaptive Shrinkage Estimation for Streaming Graphs
Nesreen Ahmed (Intel Labs) · Nick Duffield (Texas A&M University)

深度网络生成图像中的傅立叶频谱差异
Fourier Spectrum Discrepancies in Deep Network Generated Images
Tarik Dzanic (Texas A&M University) · Karan Shah (Georgia Tech) · Freddie Witherden (Texas A&M University)

稀疏加标矩阵估计中的全有或全有统计和计算相变
All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation
jean barbier (EPFL) · Nicolas Macris (EPFL) · Cynthia Rush (Columbia University)

DisARM:离散潜在变量的对立梯度估计器
DisARM: Antithetic Gradient Estimator for Discrete Latent Variables
Zhe Dong (Google Research) · Andriy Mnih (DeepMind) · George Tucker (Google Brain)

Minibatch与本地SGD的异构分布式学习
Minibatch vs Local SGD for Heterogeneous Distributed Learning
Blake Woodworth (TTIC) · Kumar Kshitij Patel (Toyota Technological Institute at Chicago) · Nati Srebro (TTI-Chicago)

可学习的搜索空间分区可扩展的黑盒优化
Scalable Black-box Optimization by Learnable Search Space Partition
Linnan Wang (Brown University) · Rodrigo Fonseca (Brown University) · Yuandong Tian (Facebook AI Research)

了解双重血统需要细粒度的偏差-方差分解
Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition
Ben Adlam (Google) · Jeffrey Pennington (Google Brain)

最佳近似-Soft-Max函数的平滑度折衷
Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions
Alessandro Epasto (Google) · Mohammad Mahdian (Google Research) · Vahab Mirrokni (Google Research NYC) · Emmanouil Zampetakis (MIT)

通过突触可塑性学习高效的任务相关表示
Learning efficient task-dependent representations with synaptic plasticity
Colin Bredenberg (New York University) · Eero Simoncelli (HHMI / New York University) · Cristina Savin (NYU)

原始-双重网格卷积神经网络
Primal-Dual Mesh Convolutional Neural Networks
Francesco Milano (ETH Zurich) · Antonio Loquercio (ETH / University of Zurich) · Antoni Rosinol (MIT) · Davide Scaramuzza (University of Zurich & ETH Zurich, Switzerland) · Luca Carlone (Massachusetts Institute of Technology)

将镜像下降重新参数化为渐变下降
Reparameterizing Mirror Descent as Gradient Descent
Ehsan Amid (University of California, Santa Cruz) · Manfred K. Warmuth (Google Brain)

多臂多武装匪徒中贪婪算法的不合理有效性
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
Mohsen Bayati (Stanford University) · Nima Hamidi (Stanford University) · Ramesh Johari (Stanford University) · Khashayar Khosravi (Google Research)

合奏的智慧:改善深度学习模型的一致性
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Lijing Wang (University of Virginia) · Dipanjan Ghosh (Industrial AI Labs, Hitachi Americas Ltd.) · Maria Gonzalez Diaz (Industrial AI Lab, Hitachi America Ltd.) · Ahmed Farahat (Industrial AI Lab, Hitachi America, Ltd. R&D) · Mahbubul Alam (Industrial AI Lab, Hitachi America, Ltd. R&D) · Chetan Gupta (Industrial AI Lab, Hitachi America R&D, Hitachi Americas Ltd.) · Jiangzhuo Chen (University of Virginia) · Madhav Marathe (Biocomplexity Institute & Initiative, University of Virginia)

在线非线性控制的信息理论后悔界
Information Theoretic Regret Bounds for Online Nonlinear Control
Sham Kakade (University of Washington & Microsoft Research) · Akshay Krishnamurthy (Microsoft) · Kendall Lowrey (University of Washington) · Motoya Ohnishi (Paul G. Allen School of Computer Science & Engineering) · Wen Sun (Microsoft Research NYC)

f-散度变分推断
f-Divergence Variational Inference
Neng Wan (University of Illinois at Urbana Champaign) · Dapeng Li (Anker Innovations) · NAIRA HOVAKIMYAN (UIUC)

通过神经投影学习身体约束
Learning Physical Constraints with Neural Projections
Shuqi Yang (Dartmouth College) · Xingzhe He (Dartmouth College) · Bo Zhu (Dartmouth College)

从具有生物学上合理的本地布线约束的压缩表示中学习稀疏代码
Learning sparse codes from compressed representations with biologically plausible local wiring constraints
Kion Fallah (Georgia Institute of Technology) · Adam A Willats (Georgia Institute of Technology and Emory University) · Ninghao Liu (Texas A&M University) · Christopher Rozell (Georgia Institute of Technology)

深旋转估计的SVD分析
An Analysis of SVD for Deep Rotation Estimation
Ameesh Makadia (Google Research) · Jake Levinson (University of Washington) · Kefan Chen (Google) · Noah Snavely (Cornell University and Google AI) · Angjoo Kanazawa (UC Berkeley) · Afshin Rostamizadeh (Google Research) · Carlos Esteves (University of Pennsylvania)

在深层网络的候选学习规则空间中表征紧急情况的表征
Characterizing emergent representations in a space of candidate learning rules for deep networks
Yinan Cao (University of Oxford) · Christopher Summerfield (University of Oxford) · Andrew Saxe (University of Oxford)

通过稳定性用亚高斯速率进行的离群值鲁棒均值估计
Outlier Robust Mean Estimation with Subgaussian Rates via Stability
Ilias Diakonikolas (UW Madison) · Daniel M. Kane (UCSD) · Ankit Pensia (University of Wisconsin-Madison)

多位专家的政策改进
Policy Improvement from Multiple Experts
Ching-An Cheng (Microsoft) · Andrey Kolobov (Microsoft Research) · Alekh Agarwal (Microsoft Research)

通过求解常微分方程来训练生成对抗网络
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Chongli Qin (DeepMind) · Yan Wu (DeepMind) · Jost Tobias Springenberg (DeepMind) · Andy Brock (DeepMind) · Jeff Donahue (DeepMind) · Timothy Lillicrap (DeepMind & UCL) · Pushmeet Kohli (DeepMind)

贪婪策略搜索的MRI实验设计
Experimental design for MRI by greedy policy search
Tim Bakker (University of Amsterdam) · Herke van Hoof (University of Amsterdam) · Max Welling (University of Amsterdam / Qualcomm AI Research)

CryptoNAS:关于ReLU预算的私人推论
CryptoNAS: Private Inference on a ReLU Budget
Zahra Ghodsi (New York University) · Akshaj Kumar Veldanda (New York University) · Brandon Reagen (New York University) · Siddharth Garg (NYU)

对小鼠的层次推理任务进行逆向工程递归神经网络解决方案
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice
Rylan Schaeffer (Harvard University) · Mikail C Khona (MIT) · Leenoy Meshulam (Massachusetts Institute of Technology MIT) · Brain Laboratory International (International Brain Laboratory) · Ila Fiete (Massachusetts Institute of Technology)

图信息瓶颈
Graph Information Bottleneck
Tailin Wu (Stanford) · Hongyu Ren (Stanford University) · Pan Li (Stanford University - Purdue University) · Jure Leskovec (Stanford University and Pinterest)

非欧氏通用逼近
Non-Euclidean Universal Approximation
Anastasis Kratsios (ETH Zürich) · Ievgen Bilokopytov (University of Manitoba)

具有对抗性或随机约束的在线次模最大化的单一配方
A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints
Omid Sadeghi (University of Washington) · Prasanna Raut (University of Washington) · Maryam Fazel (University of Washington)

神经适应属性允许识别优化的神经代码
Neural Adaptation Properties Allow Identification of Optimized Neural Codes
Luke Rast (Harvard University) · Jan Drugowitsch (Harvard Medical School)

平行随机近似近点法
Parallel Stochastic Approximate Proximal Point Methods
Hilal Asi (Stanford University) · Karan Chadha (Stanford University) · Gary Cheng (Stanford University) · John Duchi (Stanford)

Lipschitz值迭代的非策略间隔估计
Off-Policy Interval Estimation with Lipschitz Value Iteration
Ziyang Tang (UT Austin) · Yihao Feng (UT Austin) · Na Zhang (Tsinghua University) · Jian Peng (University of Illinois at Urbana-Champaign) · Qiang Liu (UT Austin)

生成神经符号机器
Generative Neurosymbolic Machines
Jindong Jiang (Rutgers University) · Sungjin Ahn (Rutgers University)

度量空间中的自适应增强学习
Provably adaptive reinforcement learning in metric spaces
Tongyi Cao (University of Massachusetts Amherst) · Akshay Krishnamurthy (Microsoft)

CogMol:使用深度生成模型的针对COVID-19的目标特异性和选择性药物设计
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
Vijil Chenthamarakshan (IBM Research) · Payel Das (IBM Research) · Samuel Hoffman (IBM Research) · Hendrik Strobelt (IBM Research) · Inkit Padhi (IBM Research) · Kar Wai Lim (IBM Singapore) · Ben Hoover (IBM) · Matteo Manica (IBM Research Zürich) · Jannis Born (IBM Research) · Teodoro Laino (IBM Research Zurich) · Aleksandra Mojsilovic (IBM Research)

有限对无穷神经网络:实证研究
Finite Versus Infinite Neural Networks: an Empirical Study
Jaehoon Lee (Google Brain) · Samuel Schoenholz (Google Brain) · Jeffrey Pennington (Google Brain) · Ben Adlam (Google) · Lechao Xiao (Google Brain) · Roman Novak (Google Brain) · Jascha Sohl-Dickstein (Google Brain)

随机潜在演员-临界:具有潜在变量模型的深度强化学习
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Alex Lee (UC Berkeley) · Anusha Nagabandi (UC Berkeley) · Pieter Abbeel (UC Berkeley & covariant.ai) · Sergey Levine (UC Berkeley)

使用重现核的鲁棒余辉图
Robust Persistence Diagrams using Reproducing Kernels
Siddharth Vishwanath (The Pennsylvania State University) · Kenji Fukumizu (Institute of Statistical Mathematics / Preferred Networks / RIKEN AIP) · Satoshi Kuriki (Institute of Statistical Mathematics) · Bharath Sriperumbudur (Penn State University)

学习与局部对比解释一致的全局透明模型
Learning Global Transparent Models consistent with Local Contrastive Explanations
Tejaswini Pedapati (IBM Research) · Avinash Balakrishnan (IBM) · Karthikeyan Shanmugam (IBM Research, NY) · Amit Dhurandhar (IBM Research)

通过附加重要性度量了解全局特征贡献
Understanding Global Feature Contributions Through Additive Importance Measures
Ian Covert (University of Washington) · Scott Lundberg (Microsoft Research) · Su-In Lee (University of Washington)

不可逆的高斯过程,用于识别神经数据中的潜在动力学结构
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
Virginia Rutten (Gatsby Computational Neuroscience Unit (UCL)) · Alberto Bernacchia (MediaTek Research) · Maneesh Sahani (Gatsby Unit, UCL) · Guillaume Hennequin (Cambridge)

带有噪声组测试的在线神经连接估计
Online Neural Connectivity Estimation with Noisy Group Testing
Anne Draelos (Duke University) · John Pearson (Duke University)

带有有限注解的医学图像分割的全局和局部特征的对比学习
Contrastive learning of global and local features for medical image segmentation with limited annotations
Krishna Chaitanya (ETH Zurich) · Ertunc Erdil (ETH Zurich) · Neerav Karani (ETH Zurich) · Ender Konukoglu (ETH Zurich)

不会过度适应身份的自动编码器
Autoencoders that don’t overfit towards the Identity
Harald Steck (Netflix)

线性上下文强盗的渐近最优本原-对偶增量算法
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Linear Contextual Bandits
Andrea Tirinzoni (Politecnico di Milano) · Matteo Pirotta (Facebook AI Research) · Marcello Restelli (Politecnico di Milano) · Alessandro Lazaric (Facebook Artificial Intelligence Research)

深度图匹配的对抗攻击
Adversarial Attacks on Deep Graph Matching
Zijie Zhang (Auburn University) · Zeru Zhang (Auburn University) · Yang Zhou (Auburn University) · Yelong Shen (Microsoft Dynamics 365 AI) · Ruoming Jin (Kent State University) · Dejing Dou (“ University of Oregon, USA”)

超越扰动:通过任意对抗性测试示例学习保证
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
Shafi Goldwasser (The Simons Institute for the Theory of Computing) · Adam Tauman Kalai (Microsoft Research) · Yael Kalai (Micr) · Omar Montasser (Toyota Technological Institute at Chicago)

保形辛和相对论最优化
Conformal Symplectic and Relativistic Optimization
Guilherme Starvaggi Franca (University of California, Berkeley) · Jeremias Sulam (Johns Hopkins University) · Daniel Robinson (Johns Hopkins University) · Rene Vidal (Johns Hopkins University, USA)

软性干预措施的一般可运输性:完整性结果
General Transportability of Soft Interventions: Completeness Results
Juan Correa (Columbia University) · Elias Bareinboim (Columbia University)

因子图文法
Factor Graph Grammars
David Chiang (University of Notre Dame) · Darcey Riley (University of Notre Dame)

多个交互神经种群的递归切换动力系统模型
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations
Joshua Glaser (Columbia) · Matthew Whiteway (Columbia University) · John Cunningham (University of Columbia) · Liam Paninski (Columbia University) · Scott Linderman (Stanford University)

CoSE:合成描边嵌入
CoSE: Compositional Stroke Embeddings
Emre Aksan (ETH Zurich) · Thomas Deselaers (Apple) · Andrea Tagliasacchi (Google Research, Brain) · Otmar Hilliges (ETH Zurich)

基于梯度的学习者的反强化学习
Inverse Reinforcement Learning from a Gradient-based Learner
Giorgia Ramponi (Politecnico di Milano) · Gianluca Drappo (Politecnico di Milano) · Marcello Restelli (Politecnico di Milano)

通过内存有效的半定性编程实现与验证无关的网络的认证
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
Sumanth Dathathri (DeepMind) · Krishnamurthy Dvijotham (DeepMind) · Alexey Kurakin (Google Brain) · Aditi Raghunathan (Stanford University) · Jonathan Uesato (DeepMind) · Rudy Bunel (Deepmind) · Shreya Shankar (Stanford University) · Jacob Steinhardt (UC Berkeley) · Ian Goodfellow (Google Brain) · Percy Liang (Stanford University) · Pushmeet Kohli (DeepMind)

适应线性背景强盗及其他之外的错误规范
Adapting to misspecification in linear contextual bandits and beyond
Dylan Foster (MIT) · Claudio Gentile (Google Research) · Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research) · Julian Zimmert (Google)

自然行为期间神经调整的有效估计
Efficient estimation of neural tuning during naturalistic behavior
Edoardo Balzani (New York University) · Kaushik Lakshminarasimhan (Columbia University) · Dora Angelaki (New York University) · Cristina Savin (NYU)

重新思考图神经网络中的池化
Rethinking pooling in graph neural networks
Diego Mesquita (Aalto University) · Amauri Souza (Federal Institute of Ceara) · Samuel Kaski (Aalto University and University of Manchester)

跨孤岛联合学习的最佳拓扑设计
Optimal Topology Design for Cross-Silo Federated Learning
Othmane MARFOQ (Inria / Accenture) · CHUAN XU (Inria Sophia Antipolis) · Giovanni Neglia (Inria) · Richard Vidal (Accenture)

用于ML编译器的可转移图形优化器
Transferable Graph Optimizers for ML Compilers
Yanqi Zhou (Google Brain) · Sudip Roy (Google) · Amirali Abdolrashidi (UC Riverside) · Daniel Wong (Carnegie Mellon University) · Peter Ma (Google) · Qiumin Xu (Google) · Hanxiao Liu (Google Brain) · Phitchaya Phothilimtha (Google Brain) · Shen Wang (Google Inc) · Anna Goldie (Google Brain / Stanford) · Azalia Mirhoseini (Google Brain) · James Laudon (Google)

测试行列式点流程
Testing Determinantal Point Processes
Khashayar Gatmiry (Massachusetts Institute of Technology) · Maryam Aliakbarpour (MIT) · Stefanie Jegelka (MIT)

梯度提升归一化流
Gradient Boosted Normalizing Flows
Robert Giaquinto (University of Minnesota) · Arindam Banerjee (University of Minnesota, Twin Cities)

信念传播神经网络
Belief Propagation Neural Networks
Jonathan Kuck (Stanford) · Shuvam Chakraborty (Stanford University) · Hao Tang (Shanghai Jiao Tong University) · Rachel Luo (Stanford University) · Jiaming Song (Stanford University) · Ashish Sabharwal (Allen Institute for AI) · Stefano Ermon (Stanford)

通过自动避让步道从重尾噪声中估计第一尖峰
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks
Jingqiu Ding (ETH Zurich) · Samuel Hopkins (UC Berkeley) · David Steurer ()

用于机器人操纵任务的基于语言的模仿学习
Language-Conditioned Imitation Learning for Robot Manipulation Tasks
Simon Stepputtis (Arizona State University) · Joseph Campbell (Arizona State University) · Mariano Phielipp (Intel AI Labs) · Stefan Lee (Oregon State University) · Chitta Baral (Arizona State University) · Heni Ben Amor (Arizona State University)

在贝叶斯神经网络中纳入可解释的输出约束
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
Wanqian Yang (Harvard University) · Lars Lorch (Harvard) · Moritz Graule (Harvard University) · Himabindu Lakkaraju (Harvard) · Finale Doshi-Velez (Harvard)

通过神经均场动力学进行网络扩散
Network Diffusions via Neural Mean-Field Dynamics
Shushan He (Georgia State University) · Hongyuan Zha (Georgia Tech) · Xiaojing Ye (Georgia State University)

贝叶斯一致性与H一致性:替代损失函数与评分函数类之间的相互作用
Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class
Mingyuan Zhang (University of Pennsylvania) · Shivani Agarwal (University of Pennsylvania)

近凸函数的核心集
Coresets for Near-Convex Functions
Morad Tukan (University of Haifa) · Alaa Maalouf (The University of Haifa) · Dan Feldman (University of Haifa)

具有线性值迭代的有效高效的奖励不可知导航
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration
Andrea Zanette (Stanford University) · Alessandro Lazaric (Facebook Artificial Intelligence Research) · Mykel J Kochenderfer (Stanford University) · Emma Brunskill (Stanford University)

用边界框浮点数在云规模上推动窄精度推理的极限
Pushing the Limits of Narrow Precision Inferencing at Cloud-Scale with Bounding-Box Floating-Point
Bita Darvish Rouhani (Microsoft) · Daniel Lo (Microsoft) · Ritchie Zhao (Microsoft) · Ming Liu (Microsoft) · Jeremy Fowers (Microsoft) · Kalin Ovtcharov (Microsoft) · Anna Vinogradsky (Caltech) · Sarah Massengill (Microsoft) · Lita Yang (Microsoft) · Ray Bittner (Microsoft Research) · Alessandro Forin (Microsoft) · Haishan Zhu (Microsoft) · Taesik Na (Microsoft) · Prerak Patel (Microsoft) · Shuai Che (Microsoft) · Lok Chand Koppaka (Microsoft) · Steve Reinhardt (Microsoft) · Sitaram Lanka (Microsoft) · XIA SONG (Microsoft) · Subhojit Som (Microsoft) · Kaustav Das (Microsoft) · Saurabh K T (Microsoft Corporation) · Eric Chung (Microsoft) · Doug Burger (Microsoft Research)

自我学习的转变,以改善注视和头部重定向
Self-Learning Transformations for Improving Gaze and Head Redirection
Yufeng Zheng (ETH Zurich) · Seonwook Park (ETH Zurich) · Xucong Zhang (ETH Zurich) · Shalini De Mello (NVIDIA) · Otmar Hilliges (ETH Zurich)

铰链损耗最小化的抗错误性
On the Error Resistance of Hinge-Loss Minimization
Kunal Talwar (Google)

具有弱线性函数近似的大型MDP中的有效规划
Efficient Planning in Large MDPs with Weak Linear Function Approximation
Roshan Shariff (University of Alberta) · Csaba Szepesvari (DeepMind / University of Alberta)

随机不确定的社会偏好产生的互惠与团队形成
Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences
Bowen Baker (OpenAI)

通过零射MEG预测建模任务对大脑中的意思表示的影响
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction
Mariya Toneva (Carnegie Mellon University) · Otilia Stretcu (Carnegie Mellon University) · Barnabas Poczos (Carnegie Mellon University) · Leila Wehbe (Carnegie Mellon University) · Tom Mitchell (Carnegie Mellon University)

PRANK:基于RANKing的运动预测
PRANK: motion Prediction based on RANKing
Yuriy Biktairov (Yandex) · Maxim Stebelev (Yandex) · Boris Yangel (Yandex) · Irina Rudenko (Yandex) · Oleh Shliazhko (Yandex)

离散多元数据的Potts-Ising模型
The Potts-Ising model for discrete multivariate data
Zahra Razaee (Cedars Sinai) · Arash Amini (UCLA)

吉布斯与人抽样
Gibbs Sampling with People
Peter Harrison (Max Planck Institute for Empirical Aesthetics) · Raga Marjieh (Max Planck Institute for Empirical Aesthetics) · Federico G Adolfi (Max-Planck Institute AE, Frankfurt, Germany) · Pol van Rijn (Max Planck Institute for Empirical Aesthetics) · Manuel Anglada-Tort (Max Planck Institute for Empirical Aesthetics) · Ofer Tchernichovski (Hunter College, CUNY) · Pauline Larrouy-Maestri (Max-Planck-Institute of Empircal Aesthetics) · Nori Jacoby (Max Planck Institute for Empirical Aesthetics)

条件和处理:改进信息理论泛化界限的技术
Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds
Hassan Hafez-Kolahi (Sharif University of Technology) · Zeinab Golgooni (Sharif University of Technology) · Shohreh Kasaei (Sharif University of Technology) · Mahdieh Soleymani (Sharif University of Technology)

通过加权经验风险最小化学习因果效应
Learning Causal Effects via Weighted Empirical Risk Minimization
Yonghan Jung (Purdue University) · Jin Tian (Iowa State University) · Elias Bareinboim (Columbia University)

强大的次高斯主成分分析和与宽度无关的Schatten填料
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing
Arun Jambulapati (Stanford University) · Jerry Li (Microsoft) · Kevin Tian (Stanford University)

论深层合奏中的幂律
On Power Laws in Deep Ensembles
Ekaterina Lobacheva (Samsung-HSE Laboratory) · Nadezhda Chirkova (Higher School of Economics, Samsung-HSE Laboratory) · Maxim Kodryan (Samsung-HSE Laboratory, National Research University Higher School of Economics) · Dmitry Vetrov (Higher School of Economics, Samsung AI Center, Moscow)

使用贝叶斯优化的未知上下文奖励的高维上下文策略搜索
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization
Qing Feng (Facebook) · Ben Letham (Facebook) · Hongzi Mao (MIT) · Eytan Bakshy (Facebook)

通过随机平滑认证的图像变换防御
Certified Defense to Image Transformations via Randomized Smoothing
Marc Fischer (ETH Zurich) · Maximilian Baader (ETH Zürich) · Martin Vechev (ETH Zurich, Switzerland)

一种元学习方法,用于(重新)发现将所需功能雕刻到神经网络的可塑性规则
A meta-learning approach to (re)discover plasticity rules that carve a desired function to a neural network
Basile Confavreux (University of Oxford) · Friedemann Zenke (Friedrich Miescher Institute) · Everton Agnes (University of Oxford) · Timothy Lillicrap (DeepMind & UCL) · Tim Vogels (Institute of Science and Technology)

Bongard-LOGO:用于人类水平概念学习和推理的新基准
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning
Weili Nie (Rice University) · Zhiding Yu (NVIDIA) · Lei Mao (NVIDIA) · Ankit Patel (Rice University) · Yuke Zhu (University of Texas - Austin) · Anima Anandkumar (NVIDIA / Caltech)

非药物干预措施对COVID-19传播有效性评估的稳健性
On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission
Mrinank Sharma (University of Oxford) · Sören Mindermann (University of Oxford) · Jan Brauner (University of Oxford) · Gavin Leech (University of Bristol) · Anna Stephenson (Harvard University) · Tomáš Gavenčiak (Independent researcher) · Jan Kulveit (University of Oxford) · Yee Whye Teh (University of Oxford, DeepMind) · Leonid Chindelevitch (Simon Fraser University) · Yarin Gal (University of Oxford)

通过有效的电机程序归纳学习绘图的抽象结构
Learning abstract structure for drawing by efficient motor program induction
Lucas Tian (MIT) · Kevin Ellis (MIT) · Marta Kryven (Massachusetts Institute of Technology) · Josh Tenenbaum (MIT)

学习认证的个人公平陈述
Learning Certified Individually Fair Representations
Anian Ruoss (ETH Zurich) · Mislav Balunovic (ETH Zurich) · Marc Fischer (ETH Zurich) · Martin Vechev (ETH Zurich, Switzerland)

Covid-19预测的可解释序列学习
Interpretable Sequence Learning for Covid-19 Forecasting
Sercan Arik (Google) · Chun-Liang Li (Google) · Martin Nikoltchev (Google) · Rajarishi Sinha (Google) · Arkady Epshteyn (Google) · Jinsung Yoon (Google) · Long Le (Google) · Vikas Menon (Google) · Shashank Singh (Google) · Yash Sonthalia (Google) · Hootan Nakhost (Google) · Leyou Zhang (Google) · Elli Kanal (Google) · Tomas Pfister (Google)

改善神经图像压缩的推断
Improving Inference for Neural Image Compression
Yibo Yang (University of California, Irivine) · Robert Bamler (University of California at Irvine) · Stephan Mandt (University of California, Irivine)

通过深度学习对高维Kolmogorov偏微分方程的参数族进行数值求解
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning
Julius Berner (University of Vienna) · Markus Dablander (University of Oxford) · Philipp Grohs (University of Vienna)

通过释义进行预训练
Pre-training via Paraphrasing
Mike Lewis (Facebook AI Research) · Marjan Ghazvininejad (Facebook AI Research) · Gargi Ghosh (Facebook) · Armen Aghajanyan (Facebook) · Sida Wang (Facebook AI Research) · Luke Zettlemoyer (University of Washington and Allen Institute for Artificial Intelligence)

通过人类注视神经注意改善自然语言处理任务
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention
Ekta Sood (University of Stuttgart, Simtech ) · Simon Tannert (Institute for Natural Language Processing, University of Stuttgart) · Philipp Mueller (VIS, University of Stuttgart) · Andreas Bulling (University of Stuttgart)

显式正则化强于隐式偏见:围绕不良全球极小值的SGD研究
Explicit Regularization is Stronger than Implicit Bias: A Study of SGD around Bad Global Minima
Shengchao Liu (MILA, Université de Montréal) · Dimitris Papailiopoulos (University of Wisconsin-Madison) · Dimitris Achlioptas (University of Athens)

隐含波动率表面的深度平滑
Deep Smoothing of the Implied Volatility Surface
Damien Ackerer (Swissquote) · Natasa Tagasovska (EPFL) · Thibault Vatter (Columbia University)

无分布的二进制分类:预测集,置信区间和校准
Distribution-free binary classification: prediction sets, confidence intervals and calibration
Chirag Gupta (Carnegie Mellon University) · Aleksandr Podkopaev (Carnegie Mellon University) · Aaditya Ramdas (CMU)

Lipschitz界线和拉普拉斯平滑可提供的鲁棒训练
Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing
Vishaal Krishnan (University of California, Riverside) · Abed AlRahman Al Makdah (University of California, Riverside) · Fabio Pasqualetti (University of California, Riverside)

多目标不可知论学习
Agnostic Learning with Multiple Objectives
Corinna Cortes (Google Research) · Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research) · Javier Gonzalvo (Google) · Dmitry Storcheus (Google Research)

随机森林的模型类依赖
Model Class Reliance for Random Forests
Gavin Smith (University of Nottingham) · Roberto Mansilla (University of Nottingham) · James Goulding (University of Nottingham)

减少概率BoxEmbeddings中的本地可识别性
Mitigating Local Identifiability in Probabilistic BoxEmbeddings
Shib Dasgupta (University of Massachusetts Amherst) · Michael Boratko (UMass Amherst) · Dongxu Zhang (University of Massachusetts Amherst) · Luke Vilnis (University of Massachusetts, Amherst) · Xiang Li (UMass Amherst) · Andrew McCallum (UMass Amherst)