ICML 2021

1,183 papers

Blind Pareto Fairness and Subgroup Robustness

Natalia L Martinez, Martin A Bertran, Afroditi Papadaki, Miguel Rodrigues, Guillermo Sapiro

Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers

Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel

Explanations for Monotonic Classifiers.

Joao Marques-Silva, Thomas Gerspacher, Martin C Cooper, Alexey Ignatiev, Nina Narodytska

Adaptive Sampling for Best Policy Identification in Markov Decision Processes

Aymen Al Marjani, Alexandre Proutiere

Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs

Weichao Mao, Kaiqing Zhang, Ruihao Zhu, David Simchi-Levi, Tamer Basar

Consistent Nonparametric Methods for Network Assisted Covariate Estimation

Xueyu Mao, Deepayan Chakrabarti, Purnamrita Sarkar

Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design

Gustavo Malkomes, Bolong Cheng, Eric H Lee, Mike Mccourt

Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity

Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li

A Sampling-Based Method for Tensor Ring Decomposition

Osman Asif Malik, Stephen Becker

Inverse Constrained Reinforcement Learning

Shehryar Malik, Usman Anwar, Alireza Aghasi, Ali Ahmed

Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels

Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro

KO codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning

Ashok V Makkuva, Xiyang Liu, Mohammad Vahid Jamali, Hessam Mahdavifar, Sewoong Oh

Near-Optimal Algorithms for Explainable k-Medians and k-Means

Konstantin Makarychev, Liren Shan

Exploiting structured data for learning contagious diseases under incomplete testing

Maggie Makar, Lauren West, David Hooper, Eric Horvitz, Erica Shenoy

Nonparametric Hamiltonian Monte Carlo

Carol Mak, Fabian Zaiser, Luke Ong

Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness

Vien V. Mai, Mikael Johansson

Domain Generalization using Causal Matching

Divyat Mahajan, Shruti Tople, Amit Sharma

Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning

Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg

Learning Interaction Kernels for Agent Systems on Riemannian Manifolds

Mauro Maggioni, Jason J Miller, Hongda Qiu, Ming Zhong

Learning to Generate Noise for Multi-Attack Robustness

Divyam Madaan, Jinwoo Shin, Sung Ju Hwang

Local Algorithms for Finding Densely Connected Clusters

Peter Macgregor, He Sun

Learning Stochastic Behaviour from Aggregate Data

Shaojun Ma, Shu Liu, Hongyuan Zha, Haomin Zhou

Neural-Pull: Learning Signed Distance Function from Point clouds by Learning to Pull Space onto Surface

Baorui Ma, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker

Meta-Cal: Well-controlled Post-hoc Calibration by Ranking

Xingchen Ma, Matthew B. Blaschko

Value Iteration in Continuous Actions, States and Time

Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg

HyperHyperNetwork for the Design of Antenna Arrays

Shahar Lutati, Lior Wolf

Trajectory Diversity for Zero-Shot Coordination

Andrei Lupu, Brandon Cui, Hengyuan Hu, Jakob Foerster

GraphDF: A Discrete Flow Model for Molecular Graph Generation

Youzhi Luo, Keqiang Yan, Shuiwang Ji

Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies.

Denis Lukovnikov, Asja Fischer

On Monotonic Linear Interpolation of Neural Network Parameters

James R Lucas, Juhan Bae, Michael R Zhang, Stanislav Fort, Richard Zemel

ACE: Explaining cluster from an adversarial perspective

Yang Young Lu, Timothy C Yu, Giancarlo Bonora, William Stafford Noble

Variance Reduced Training with Stratified Sampling for Forecasting Models

Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa

Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification

Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama

DANCE: Enhancing saliency maps using decoys

Yang Young Lu, Wenbo Guo, Xinyu Xing, William Stafford Noble

Optimal Complexity in Decentralized Training

Yucheng Lu, Christopher De Sa

HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture

Qian Lou, Lei Jiang

Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach

Federico López, Beatrice Pozzetti, Steve Trettel, Michael Strube, Anna Wienhard

Joint Online Learning and Decision-making via Dual Mirror Descent

Alfonso Lobos, Paul Grigas, Zheng Wen

Relative Positional Encoding for Transformers with Linear Complexity

Antoine Liutkus, Ondřej Cı́fka, Shih-Lun Wu, Umut Simsekli, Yi-Hsuan Yang

Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning

Xutong Liu, Jinhang Zuo, Xiaowei Chen, Wei Chen, John C. S. Lui

Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent

Kangqiao Liu, Liu Ziyin, Masahito Ueda

Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport

Lewis Liu, Yufeng Zhang, Zhuoran Yang, Reza Babanezhad, Zhaoran Wang

Group Fisher Pruning for Practical Network Compression

Liyang Liu, Shilong Zhang, Zhanghui Kuang, Aojun Zhou, Jing-Hao Xue

Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?

Ning Liu, Geng Yuan, Zhengping Che, Xuan Shen, Xiaolong Ma

A Sharp Analysis of Model-based Reinforcement Learning with Self-Play

Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin

Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training

Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy

Watermarking Deep Neural Networks with Greedy Residuals

Hanwen Liu, Zhenyu Weng, Yuesheng Zhu

Leveraging Public Data for Practical Private Query Release

Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, Steven Wu

Learning Deep Neural Networks under Agnostic Corrupted Supervision

Boyang Liu, Mengying Sun, Ding Wang, Pang-Ning Tan, Jiayu Zhou

SagaNet: A Small Sample Gated Network for Pediatric Cancer Diagnosis

Yuhan Liu, Shiliang Sun

How Do Adam and Training Strategies Help BNNs Optimization

Zechun Liu, Zhiqiang Shen, Shichao Li, Koen Helwegen, Dong Huang

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices

Evan Z Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn

On Robust Mean Estimation under Coordinate-level Corruption

Zifan Liu, Jong Ho Park, Theodoros Rekatsinas, Christos Tzamos

Temporal Difference Learning as Gradient Splitting

Rui Liu, Alex Olshevsky

Selfish Sparse RNN Training

Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy

A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization

Risheng Liu, Xuan Liu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang

From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments

Yiwei Liu, Jiamou Liu, Kaibin Wan, Zhan Qin, Zijian Zhang

Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition

Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu

One Pass Late Fusion Multi-view Clustering

Xinwang Liu, Li Liu, Qing Liao, Siwei Wang, Yi Zhang

Elastic Graph Neural Networks

Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu

Cooperative Exploration for Multi-Agent Deep Reinforcement Learning

Iou-Jen Liu, Unnat Jain, Raymond A Yeh, Alexander Schwing

Stochastic Iterative Graph Matching

Linfeng Liu, Michael C Hughes, Soha Hassoun, Liping Liu

Heterogeneous Risk Minimization

Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen

Event Outlier Detection in Continuous Time

Siqi Liu, Milos Hauskrecht

Just Train Twice: Improving Group Robustness without Training Group Information

Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh

Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks

Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao

Dynamic Game Theoretic Neural Optimizer

Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou

Learning by Turning: Neural Architecture Aware Optimisation

Yang Liu, Jeremy Bernstein, Markus Meister, Yisong Yue

APS: Active Pretraining with Successor Features

Hao Liu, Pieter Abbeel

Understanding Instance-Level Label Noise: Disparate Impacts and Treatments

Yang Liu

The Earth Mover’s Pinball Loss: Quantiles for Histogram-Valued Regression

Florian List

Phase Transitions, Distance Functions, and Implicit Neural Representations

Yaron Lipman

Active Learning of Continuous-time Bayesian Networks through Interventions

Dominik Linzner, Heinz Koeppl

Tractable structured natural-gradient descent using local parameterizations

Wu Lin, Frank Nielsen, Khan Mohammad Emtiyaz, Mark Schmidt

Generative Causal Explanations for Graph Neural Networks

Wanyu Lin, Hao Lan, Baochun Li

Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Tao LIN, Sai Praneeth Karimireddy, Sebastian Stich, Martin Jaggi

Straight to the Gradient: Learning to Use Novel Tokens for Neural Text Generation

Xiang Lin, Simeng Han, Shafiq Joty

Making transport more robust and interpretable by moving data through a small number of anchor points

Chi-Heng Lin, Mehdi Azabou, Eva Dyer

Debiasing a First-order Heuristic for Approximate Bi-level Optimization

Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Q Davis, Adrian Weller

Guided Exploration with Proximal Policy Optimization using a Single Demonstration

Gabriele Libardi, Gianni De Fabritiis, Sebastian Dittert

Information Obfuscation of Graph Neural Networks

Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi Jaakkola, Geoffrey J. Gordon

Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning

Tung-Che Liang, Jin Zhou, Yun-Sheng Chan, Tsung-Yi Ho, Krishnendu Chakrabarty

Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability

Kaizhao Liang, Jacky Y Zhang, Boxin Wang, Zhuolin Yang, Sanmi Koyejo

Towards Understanding and Mitigating Social Biases in Language Models

Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, Ruslan Salakhutdinov

A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance

Xiaoyu Li, Zhenxun Zhuang, Francesco Orabona

TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models

Zhuohan Li, Siyuan Zhuang, Shiyuan Guo, Danyang Zhuo, Hao Zhang

Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-Norm Least Squares Estimator

Zeng Li, Chuanlong Xie, Qinwen Wang

Shi Li, Jiayi Xian

FILTRA: Rethinking Steerable CNN by Filter Transform

Bo Li, Qili Wang, Gim Hee Lee

Communication-Efficient Distributed SVD via Local Power Iterations

Xiang Li, Shusen Wang, Kun Chen, Zhihua Zhang

Distributionally Robust Optimization with Markovian Data

Mengmeng Li, Tobias Sutter, Daniel Kuhn

The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks

Xiaocheng Li, Chunlin Sun, Yinyu Ye

Testing DNN-based Autonomous Driving Systems under Critical Environmental Conditions

Zhong Li, Minxue Pan, Tian Zhang, Xuandong Li

Partially Observed Exchangeable Modeling

Yang Li, Junier Oliva

Active Feature Acquisition with Generative Surrogate Models

Yang Li, Junier Oliva

Training Graph Neural Networks with 1000 Layers

Guohao Li, Matthias Müller, Bernard Ghanem, Vladlen Koltun

Mixed Cross Entropy Loss for Neural Machine Translation

Haoran Li, Wei Lu

A Novel Method to Solve Neural Knapsack Problems

Duanshun Li, Jing Liu, Dongeun Lee, Ali Seyedmazloom, Giridhar Kaushik

Provably End-to-end Label-noise Learning without Anchor Points

Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama

Sharper Generalization Bounds for Clustering

Shaojie Li, Yong Liu