ICML 2021

1,183 papers

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes

Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Gordon Wilson

Statistical Estimation from Dependent Data

Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis

Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations

Angeliki Kamoutsi, Goran Banjac, John Lygeros

Optimal Off-Policy Evaluation from Multiple Logging Policies

Nathan Kallus, Yuta Saito, Masatoshi Uehara

Projection techniques to update the truncated SVD of evolving matrices with applications

Vasileios Kalantzis, Georgios Kollias, Shashanka Ubaru, Athanasios N. Nikolakopoulos, Lior Horesh

A Differentiable Point Process with Its Application to Spiking Neural Networks

Hiroshi Kajino

Practical and Private (Deep) Learning Without Sampling or Shuffling

Peter Kairouz, Brendan Mcmahan, Shuang Song, Om Thakkar, Abhradeep Thakurta

The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation

Peter Kairouz, Ziyu Liu, Thomas Steinke

Training Recurrent Neural Networks via Forward Propagation Through Time

Anil Kag, Venkatesh Saligrama

A Nullspace Property for Subspace-Preserving Recovery

Mustafa D Kaba, Chong You, Daniel P Robinson, Enrique Mallada, René Vidal

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning

Yonghan Jung, Jin Tian, Elias Bareinboim

Detection of Signal in the Spiked Rectangular Models

Ji Hyung Jung, Hye Won Chung, Ji Oon Lee

Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits

Kwang-Sung Jun, Lalit Jain, Blake Mason, houssam nassif

On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models

Peizhong Ju, Xiaojun Lin, Ness Shroff

Isometric Gaussian Process Latent Variable Model for Dissimilarity Data

Martin Jørgensen, Soren Hauberg

Provable Lipschitz Certification for Generative Models

Matt Jordan, Alex Dimakis

Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information

Changhun Jo, Kangwook Lee

Adversarial Option-Aware Hierarchical Imitation Learning

Mingxuan Jing, Wenbing Huang, Fuchun Sun, Xiaojian Ma, Tao Kong

Is Pessimism Provably Efficient for Offline RL?

Ying Jin, Zhuoran Yang, Zhaoran Wang

MOTS: Minimax Optimal Thompson Sampling

Tianyuan Jin, Pan Xu, Jieming SHI, Xiaokui Xiao, Quanquan Gu

Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits

Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao

Towards Tight Bounds on the Sample Complexity of Average-reward MDPs

Yujia Jin, Aaron Sidford

Optimal Streaming Algorithms for Multi-Armed Bandits

Tianyuan Jin, Keke Huang, Jing Tang, Xiaokui Xiao

Characterizing Structural Regularities of Labeled Data in Overparameterized Models

Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, Michael C Mozer

Emphatic Algorithms for Deep Reinforcement Learning

Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel

Active Covering

Heinrich Jiang, Afshin Rostamizadeh

Online Selection Problems against Constrained Adversary

Zhihao Jiang, Pinyan Lu, Zhihao Gavin Tang, Yuhao Zhang

The Emergence of Individuality

Jiechuan Jiang, Zongqing Lu

Single Pass Entrywise-Transformed Low Rank Approximation

Yifei Jiang, Yi Li, Yiming Sun, Jiaxin Wang, David Woodruff

Streaming and Distributed Algorithms for Robust Column Subset Selection

Shuli Jiang, Dennis Li, Irene Mengze Li, Arvind V Mahankali, David Woodruff

Approximation Theory of Convolutional Architectures for Time Series Modelling

Haotian Jiang, Zhong Li, Qianxiao Li

Monotonic Robust Policy Optimization with Model Discrepancy

Yuankun Jiang, Chenglin Li, Wenrui Dai, Junni Zou, Hongkai Xiong

Prioritized Level Replay

Minqi Jiang, Edward Grefenstette, Tim Rocktäschel

Self-Damaging Contrastive Learning

Ziyu Jiang, Tianlong Chen, Bobak J Mortazavi, Zhangyang Wang

Multi-Dimensional Classification via Sparse Label Encoding

Bin-Bin Jia, Min-ling Zhang

Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh

Efficient Statistical Tests: A Neural Tangent Kernel Approach

Sheng Jia, Ehsan Nezhadarya, Yuhuai Wu, Jimmy Ba

Bilevel Optimization: Convergence Analysis and Enhanced Design

Kaiyi Ji, Junjie Yang, Yingbin Liang

Marginalized Stochastic Natural Gradients for Black-Box Variational Inference

Geng Ji, Debora Sujono, Erik B Sudderth

Fast margin maximization via dual acceleration

Ziwei Ji, Nathan Srebro, Matus Telgarsky

Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction

Aditi Jha, Michael J. Morais, Jonathan W Pillow

DeepReDuce: ReLU Reduction for Fast Private Inference

Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen

Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit

Objective Bound Conditional Gaussian Process for Bayesian Optimization

Taewon Jeong, Heeyoung Kim

Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics

Vivek Jayaram, John Thickstun

In-Database Regression in Input Sparsity Time

Rajesh Jayaram, Alireza Samadian, David Woodruff, Peng Ye

Policy Gradient Bayesian Robust Optimization for Imitation Learning

Zaynah Javed, Daniel S Brown, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna

Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization

Stanislaw Jastrzebski, Devansh Arpit, Oliver Astrand, Giancarlo B Kerg, Huan Wang

Inverse Decision Modeling: Learning Interpretable Representations of Behavior

Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar

Improved Regret Bounds of Bilinear Bandits using Action Space Analysis

Kyoungseok Jang, Kwang-Sung Jun, Se-Young Yun, Wanmo Kang

Feature Clustering for Support Identification in Extreme Regions

Hamid JALALZAI, Rémi Leluc

Fairness for Image Generation with Uncertain Sensitive Attributes

Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann, Alex Dimakis, Eric Price

Instance-Optimal Compressed Sensing via Posterior Sampling

Ajil Jalal, Sushrut Karmalkar, Alex Dimakis, Eric Price

Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free

Ayush Jain, Alon Orlitsky

Alternative Microfoundations for Strategic Classification

Meena Jagadeesan, Celestine Mendler-Dünner, Moritz Hardt

Local Correlation Clustering with Asymmetric Classification Errors

Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev, Yury Makarychev

Imitation by Predicting Observations

Andrew Jaegle, Yury Sulsky, Arun Ahuja, Jake Bruce, Rob Fergus

Perceiver: General Perception with Iterative Attention

Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman

How to Learn when Data Reacts to Your Model: Performative Gradient Descent

Zachary Izzo, Lexing Ying, James Zou

What Are Bayesian Neural Network Posteriors Really Like?

Pavel Izmailov, Sharad Vikram, Matthew D Hoffman, Andrew Gordon Gordon Wilson

Distributed Second Order Methods with Fast Rates and Compressed Communication

Rustem Islamov, Xun Qian, Peter Richtarik

Randomized Exploration in Reinforcement Learning with General Value Function Approximation

Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang

Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

Shariq Iqbal, Christian A Schroeder De Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson

Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization

Hedda Cohen Indelman, Tamir Hazan

Active Learning for Distributionally Robust Level-Set Estimation

Yu Inatsu, Shogo Iwazaki, Ichiro Takeuchi

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning

Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Khan Mohammad Emtiyaz

Selecting Data Augmentation for Simulating Interventions

Maximilian Ilse, Jakub M Tomczak, Patrick Forré

Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization

Shahana Ibrahim, Xiao Fu

LieTransformer: Equivariant Self-Attention for Lie Groups

Michael J Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh

Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions

Todd Huster, Jeremy Cohen, Zinan Lin, Kevin Chan, Charles Kamhoua

Hyperparameter Selection for Imitation Learning

Leonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk

Neural Pharmacodynamic State Space Modeling

Zeshan M Hussain, Rahul G. Krishnan, David Sontag

Generative Adversarial Transformers

Drew A Hudson, Larry Zitnick

Learning and Planning in Complex Action Spaces

Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt

Accurate Post Training Quantization With Small Calibration Sets

Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, Daniel Soudry

Projection Robust Wasserstein Barycenters

Minhui Huang, Shiqian Ma, Lifeng LAI

A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance

Minhui Huang, Shiqian Ma, Lifeng LAI

STRODE: Stochastic Boundary Ordinary Differential Equation

Hengguan Huang, Hongfu Liu, Hao Wang, Chang Xiao, Ye Wang

FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis

Baihe Huang, Xiaoxiao Li, Zhao Song, Xin Yang

A Novel Sequential Coreset Method for Gradient Descent Algorithms

Jiawei Huang, Ruomin Huang, Wenjie Liu, Nikolaos Freris, Hu Ding

On Recovering from Modeling Errors Using Testing Bayesian Networks

Haiying Huang, Adnan Darwiche

A Scalable Deterministic Global Optimization Algorithm for Clustering Problems

Kaixun Hua, Mingfei Shi, Yankai Cao

Generalizable Episodic Memory for Deep Reinforcement Learning

Hao Hu, Jianing Ye, Guangxiang Zhu, Zhizhou Ren, Chongjie Zhang

Off-Belief Learning

Hengyuan Hu, Adam Lerer, Brandon Cui, Luis Pineda, Noam Brown

On the Random Conjugate Kernel and Neural Tangent Kernel

Zhengmian Hu, Heng Huang

Near-Optimal Representation Learning for Linear Bandits and Linear RL

Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang

The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets

Ya-Ping Hsieh, Panayotis Mertikopoulos, Volkan Cevher

Federated Learning of User Verification Models Without Sharing Embeddings

Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga

Chebyshev Polynomial Codes: Task Entanglement-based Coding for Distributed Matrix Multiplication

Sangwoo Hong, Heecheol Yang, Youngseok Yoon, Taehyun Cho, Jungwoo Lee

Latent Programmer: Discrete Latent Codes for Program Synthesis

Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer

Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes

Peter Holderrieth, Michael J Hutchinson, Yee Whye Teh

Learning Curves for Analysis of Deep Networks

Derek Hoiem, Tanmay Gupta, Zhizhong Li, Michal Shlapentokh-Rothman

MC-LSTM: Mass-Conserving LSTM

Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz, Christina Halmich, Markus Holzleitner

Multiplicative Noise and Heavy Tails in Stochastic Optimization

Liam Hodgkinson, Michael Mahoney

Trees with Attention for Set Prediction Tasks

Roy Hirsch, Ran Gilad-Bachrach

Optimizing Black-box Metrics with Iterative Example Weighting

Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan, Mahdi Milani Fard, Sanmi Koyejo

Learning Representations by Humans, for Humans

Sophie Hilgard, Nir Rosenfeld, Mahzarin R Banaji, Jack Cao, David Parkes

Muesli: Combining Improvements in Policy Optimization

Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt

Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity

Ryan Henderson, Djork-Arné Clevert, Floriane Montanari

Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging

Amélie Heliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier