ICML 2021

1,183 papers

A Functional Perspective on Learning Symmetric Functions with Neural Networks

Aaron Zweig, Joan Bruna

On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients

Difan Zou, Quanquan Gu

Provable Robustness of Adversarial Training for Learning Halfspaces with Noise

Difan Zou, Spencer Frei, Quanquan Gu

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

Luisa M Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen

Contrastive Learning Inverts the Data Generating Process

Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel

Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning

Matthieu Zimmer, Claire Glanois, Umer Siddique, Paul Weng

Recovering AES Keys with a Deep Cold Boot Attack

Itamar Zimerman, Eliya Nachmani, Lior Wolf

Demystifying Inductive Biases for (Beta-)VAE Based Architectures

Dominik Zietlow, Michal Rolinek, Georg Martius

Accumulated Decoupled Learning with Gradient Staleness Mitigation for Convolutional Neural Networks

HUIPING ZHUANG, Zhenyu Weng, Fulin Luo, Toh Kar-Ann, Haizhou Li

Commutative Lie Group VAE for Disentanglement Learning

Xinqi Zhu, Chang Xu, Dacheng Tao

Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels

Zhaowei Zhu, Yiwen Song, Yang Liu

Few-shot Language Coordination by Modeling Theory of Mind

Hao Zhu, Graham Neubig, Yonatan Bisk

Spectral vertex sparsifiers and pair-wise spanners over distributed graphs

Chunjiang Zhu, Qinqing Liu, Jinbo Bi

Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Zhuangdi Zhu, Junyuan Hong, Jiayu Zhou

Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm

Mingkang Zhu, Tianlong Chen, Zhangyang Wang

Examining and Combating Spurious Features under Distribution Shift

Chunting Zhou, Xuezhe Ma, Paul Michel, Graham Neubig

Asymmetric Loss Functions for Learning with Noisy Labels

Xiong Zhou, Xianming Liu, Junjun Jiang, Xin Gao, Xiangyang Ji

Towards Defending against Adversarial Examples via Attack-Invariant Features

Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng

Incentivized Bandit Learning with Self-Reinforcing User Preferences

Tianchen Zhou, Jia Liu, Chaosheng Dong, Jingyuan Deng

Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations

Fan Zhou, Ping Li

Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation

Aurick Zhou, Sergey Levine

Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping

Dongruo Zhou, Jiafan He, Quanquan Gu

Towards Distraction-Robust Active Visual Tracking

Fangwei Zhong, peng sun, Wenhan Luo, Tingyun Yan, Yizhou Wang

Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions

Zixin Zhong, Wang Chi Cheung, Vincent Tan

How Framelets Enhance Graph Neural Networks

Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yuguang Wang, Pietro Lió

Two Heads are Better Than One: Hypergraph-Enhanced Graph Reasoning for Visual Event Ratiocination

Wenbo Zheng, Lan Yan, Chao Gou, Fei-Yue Wang

Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation

Renjie Zheng, Junkun Chen, Mingbo Ma, Liang Huang

Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks

Xin Zhao, Zeru Zhang, Zijie Zhang, Lingfei Wu, Jiayin Jin

Few-Shot Neural Architecture Search

Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, Tian Guo

Calibrate Before Use: Improving Few-shot Performance of Language Models

Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh

Joining datasets via data augmentation in the label space for neural networks

Junbo Zhao, Mingfeng Ou, Linji Xue, Yunkai Cui, Sai Wu

Dataset Condensation with Differentiable Siamese Augmentation

Bo Zhao, Hakan Bilen

Learning to Rehearse in Long Sequence Memorization

Zhu Zhang, Chang Zhou, Jianxin Ma, Zhijie Lin, Jingren Zhou

Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity

Zihan Zhang, Yuan Zhou, Xiangyang Ji

Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation

Qian Zhang, Yilin Zheng, Jean Honorio

Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference

Shumao Zhang, Pengchuan Zhang, Thomas Y Hou

Breaking the Deadly Triad with a Target Network

Shangtong Zhang, Hengshuai Yao, Shimon Whiteson

World Model as a Graph: Learning Latent Landmarks for Planning

Lunjun Zhang, Ge Yang, Bradly C Stadie

MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration

Jin Zhang, Jianhao Wang, Hao Hu, Tong Chen, Yingfeng Chen

Matrix Sketching for Secure Collaborative Machine Learning

Mengjiao Zhang, Shusen Wang

Average-Reward Off-Policy Policy Evaluation with Function Approximation

Shangtong Zhang, Yi Wan, Richard S Sutton, Shimon Whiteson

Deep Coherent Exploration for Continuous Control

Yijie Zhang, Herke van Hoof

iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients

Miao Zhang, Steven W. Su, Shirui Pan, Xiaojun Chang, Ehsan M Abbasnejad

Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution

Zhaoyang Zhang, Wenqi Shao, Jinwei Gu, Xiaogang Wang, Ping Luo

On-Policy Deep Reinforcement Learning for the Average-Reward Criterion

Yiming Zhang, Keith W Ross

Towards Better Robust Generalization with Shift Consistency Regularization

Shufei Zhang, Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Rui Zhang

Quantile Bandits for Best Arms Identification

Mengyan Zhang, Cheng Soon Ong

Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization

Yivan Zhang, Gang Niu, Masashi Sugiyama

FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning

Tianhao Zhang, Yueheng Li, Chen Wang, Guangming Xie, Zongqing Lu

Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation

Jiawei Zhang, Linyi Li, Huichen Li, Xiaolu Zhang, Shuang Yang

Learning from Noisy Labels with No Change to the Training Process

Mingyuan Zhang, Jane Lee, Shivani Agarwal

PAPRIKA: Private Online False Discovery Rate Control

Wanrong Zhang, Gautam Kamath, Rachel Cummings

Probabilistic Generating Circuits

Honghua Zhang, Brendan Juba, Guy Van den Broeck

Poolingformer: Long Document Modeling with Pooling Attention

Hang Zhang, Yeyun Gong, Yelong Shen, Weisheng Li, Jiancheng Lv

Understanding Failures in Out-of-Distribution Detection with Deep Generative Models

Lily Zhang, Mark Goldstein, Rajesh Ranganath

Bayesian Attention Belief Networks

Shujian Zhang, Xinjie Fan, Bo Chen, Mingyuan Zhou

Near Optimal Reward-Free Reinforcement Learning

Zihan Zhang, Simon Du, Xiangyang Ji

Robust Policy Gradient against Strong Data Corruption

Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun

Efficient Lottery Ticket Finding: Less Data is More

Zhenyu Zhang, Xuxi Chen, Tianlong Chen, Zhangyang Wang

Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons

Bohang Zhang, Tianle Cai, Zhou Lu, Di He, Liwei Wang

Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?

Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville

DORO: Distributional and Outlier Robust Optimization

Runtian Zhai, Chen Dan, Zico Kolter, Pradeep Ravikumar

DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian

You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling

Zhanpeng Zeng, Yunyang Xiong, Sathya Ravi, Shailesh Acharya, Glenn M Fung

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, Stephane Deny

Learning Binary Decision Trees by Argmin Differentiation

Valentina Zantedeschi, Matt Kusner, Vlad Niculae

Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL

Andrea Zanette

Grey-box Extraction of Natural Language Models

Santiago Zanella-Beguelin, Shruti Tople, Andrew Paverd, Boris Köpf

Three Operator Splitting with a Nonconvex Loss Function

Alp Yurtsever, Varun Mangalick, Suvrit Sra

Federated Composite Optimization

Honglin Yuan, Manzil Zaheer, Sashank Reddi

On Explainability of Graph Neural Networks via Subgraph Explorations

Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji

Neural Tangent Generalization Attacks

Chia-Hung Yuan, Shan-Hung Wu

Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity

Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang

Large Scale Private Learning via Low-rank Reparametrization

Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-yan Liu

Learning Generalized Intersection Over Union for Dense Pixelwise Prediction

Jiaqian Yu, Jingtao Xu, Yiwei Chen, Weiming Li, Qiang Wang

Deep Latent Graph Matching

Tianshu Yu, Runzhong Wang, Junchi Yan, baoxin Li

Whittle Networks: A Deep Likelihood Model for Time Series

Zhongjie Yu, Fabrizio G Ventola, Kristian Kersting

Provably Efficient Algorithms for Multi-Objective Competitive RL

Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra

DAGs with No Curl: An Efficient DAG Structure Learning Approach

Yue Yu, Tian Gao, Naiyu Yin, Qiang Ji

Exponentially Many Local Minima in Quantum Neural Networks

Xuchen You, Xiaodi Wu

LogME: Practical Assessment of Pre-trained Models for Transfer Learning

Kaichao You, Yong Liu, Jianmin Wang, Mingsheng Long

Graph Contrastive Learning Automated

Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang

Lower-Bounded Proper Losses for Weakly Supervised Classification

Shuhei M Yoshida, Takashi Takenouchi, Masashi Sugiyama

Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k^2) Rate on Squared Gradient Norm

TaeHo Yoon, Ernest K Ryu

Autoencoding Under Normalization Constraints

Sangwoong Yoon, Yung-kyun Noh, Frank Park

Federated Continual Learning with Weighted Inter-client Transfer

Jaehong Yoon, Wonyong Jeong, Giwoong Lee, Eunho Yang, Sung Ju Hwang

Adversarial Purification with Score-based Generative Models

Jongmin Yoon, Sung Ju Hwang, Juho Lee

Conditional Temporal Neural Processes with Covariance Loss

Boseon Yoo, Jiwoo Lee, Janghoon Ju, Seijun Chung, Soyeon Kim

SinIR: Efficient General Image Manipulation with Single Image Reconstruction

Jihyeong Yoo, Qifeng Chen

Path Planning using Neural A* Search

Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain, Mai Nishimura, Asako Kanezaki

Distributed Nyström Kernel Learning with Communications

Rong Yin, Weiping Wang, Dan Meng

Continuous-time Model-based Reinforcement Learning

Cagatay Yildiz, Markus Heinonen, Harri Lahdesmaki

Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints

Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Tianyou Chai

Improved OOD Generalization via Adversarial Training and Pretraing

Mingyang Yi, Lu Hou, Jiacheng Sun, Lifeng Shang, Xin Jiang

From Local Structures to Size Generalization in Graph Neural Networks

Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron

Neighborhood Contrastive Learning Applied to Online Patient Monitoring

Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch

Improving Gradient Regularization using Complex-Valued Neural Networks

Eric C Yeats, Yiran Chen, Hai Li

Break-It-Fix-It: Unsupervised Learning for Program Repair

Michihiro Yasunaga, Percy Liang

Elementary superexpressive activations

Dmitry Yarotsky

Reinforcement Learning with Prototypical Representations

Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto