ICML 2021

1,183 papers

Approximate Group Fairness for Clustering

Bo Li, Lijun Li, Ankang Sun, Chenhao Wang, Yingfan Wang

Quantization Algorithms for Random Fourier Features

Xiaoyun Li, Ping Li

Ditto: Fair and Robust Federated Learning Through Personalization

Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith

MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning

Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H Pong, Aurick Zhou

Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph

Gen Li, Yuantao Gu

Privacy-Preserving Feature Selection with Secure Multiparty Computation

Xiling Li, Rafael Dowsley, Martine De Cock

A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration

Yuhang Li, Shikuang Deng, Xin Dong, Ruihao Gong, Shi Gu

Winograd Algorithm for AdderNet

Wenshuo Li, Hanting Chen, Mingqiang Huang, Xinghao Chen, Chunjing XU

Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning

Gen Li, Changxiao Cai, Yuxin Chen, Yuantao Gu, Yuting Wei

PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization

Zhize Li, Hongyan Bao, Xiangliang Zhang, Peter Richtarik

Run-Sort-ReRun: Escaping Batch Size Limitations in Sliced Wasserstein Generative Models

José Lezama, Wei Chen, Qiang Qiu

BASE Layers: Simplifying Training of Large, Sparse Models

Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, Luke Zettlemoyer

Improved, Deterministic Smoothing for L_1 Certified Robustness

Alexander J Levine, Soheil Feizi

Strategic Classification Made Practical

Sagi Levanon, Nir Rosenfeld

SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data

Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V. Bonilla, Theodoros Damoulas

Learning to Price Against a Moving Target

Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng, Pratik Worah

Globally-Robust Neural Networks

Klas Leino, Zifan Wang, Matt Fredrikson

Better Training using Weight-Constrained Stochastic Dynamics

Benedict Leimkuhler, Tiffany J Vlaar, Timothée Pouchon, Amos Storkey

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

Joel Z Leibo, Edgar A Dueñez-Guzman, Alexander Vezhnevets, John P Agapiou, Peter Sunehag

Stability and Generalization of Stochastic Gradient Methods for Minimax Problems

Yunwen Lei, ZHENHUAN YANG, Tianbao Yang, Yiming Ying

Near-Optimal Linear Regression under Distribution Shift

Qi Lei, Wei Hu, Jason Lee

PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training

Kimin Lee, Laura M Smith, Pieter Abbeel

Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously

Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang, Xiaojin Zhang

SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel

OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation

Jongmin Lee, Wonseok Jeon, Byungjun Lee, Joelle Pineau, Kee-eung Kim

Continual Learning in the Teacher-Student Setup: Impact of Task Similarity

Sebastian Lee, Sebastian Goldt, Andrew Saxe

Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification

Dong Hoon Lee, Sae-Young Chung

On-the-fly Rectification for Robust Large-Vocabulary Topic Inference

Moontae Lee, Sungjun Cho, Kun Dong, David Mimno, David Bindel

Fair Selective Classification Via Sufficiency

Joshua K Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda

Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer

Seungwon Lee, Sima Behpour, Eric Eaton

Gaussian Process-Based Real-Time Learning for Safety Critical Applications

Armin Lederer, Alejandro J Ordóñez Conejo, Korbinian A Maier, Wenxin Xiao, Jonas Umlauft

LAMDA: Label Matching Deep Domain Adaptation

Trung Le, Tuan Nguyen, nhật Hồ, Hung Bui, Dinh Phung

Improved Regret Bound and Experience Replay in Regularized Policy Iteration

Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvári

MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space

Sophie C. Laturnus, Philipp Berens

CountSketches, Feature Hashing and the Median of Three

Kasper Green Larsen, Rasmus Pagh, Jakub Tětek

Efficient Message Passing for 0–1 ILPs with Binary Decision Diagrams

Jan-Hendrik Lange, Paul Swoboda

Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch)

Hunter Lang, David Sontag, Aravindan Vijayaraghavan

Discovering symbolic policies with deep reinforcement learning

Mikel Landajuela, Brenden K Petersen, Sookyung Kim, Claudio P Santiago, Ruben Glatt

Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions

Tal Lancewicki, Shahar Segal, Tomer Koren, Yishay Mansour

Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix

Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher

Model Fusion for Personalized Learning

Thanh Chi Lam, Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet

Generalization Bounds in the Presence of Outliers: a Median-of-Means Study

Pierre Laforgue, Guillaume Staerman, Stéphan Clémençon

Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality

Jonathan Lacotte, Yifei Wang, Mert Pilanci

On the price of explainability for some clustering problems

Eduardo S Laber, Lucas Murtinho

ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks

Jungmin Kwon, Jeongseop Kim, Hyunseo Park, In Kwon Choi

Targeted Data Acquisition for Evolving Negotiation Agents

Minae Kwon, Siddharth Karamcheti, Mariano-Florentino Cuellar, Dorsa Sadigh

Meta-Thompson Sampling

Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-wei Hsu, Martin Mladenov

A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples

Christian Kümmerle, Claudio M. Verdun

Implicit rate-constrained optimization of non-decomposable objectives

Abhishek Kumar, Harikrishna Narasimhan, Andrew Cotter

Bayesian Structural Adaptation for Continual Learning

Abhishek Kumar, Sunabha Chatterjee, Piyush Rai

Differentially Private Bayesian Inference for Generalized Linear Models

Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela

Near-Optimal Confidence Sequences for Bounded Random Variables

Arun K Kuchibhotla, Qinqing Zheng

Out-of-Distribution Generalization via Risk Extrapolation (REx)

David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas

Adapting to misspecification in contextual bandits with offline regression oracles

Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey

Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning

Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski

ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks

Dmitry Kovalev, Egor Shulgin, Peter Richtarik, Alexander V Rogozin, Alexander Gasnikov

Offline Reinforcement Learning with Fisher Divergence Critic Regularization

Ilya Kostrikov, Rob Fergus, Jonathan Tompson, Ofir Nachum

High Confidence Generalization for Reinforcement Learning

James Kostas, Yash Chandak, Scott M Jordan, Georgios Theocharous, Philip Thomas

Active Testing: Sample-Efficient Model Evaluation

Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth

NeRF-VAE: A Geometry Aware 3D Scene Generative Model

Adam R Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider

Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size

Jack Kosaian, Amar Phanishayee, Matthai Philipose, Debadeepta Dey, Rashmi Vinayak

Kernel Stein Discrepancy Descent

Anna Korba, Pierre-Cyril Aubin-Frankowski, Szymon Majewski, Pierre Ablin

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?

Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann

A Distribution-dependent Analysis of Meta Learning

Mikhail Konobeev, Ilja Kuzborskij, Csaba Szepesvári

Consensus Control for Decentralized Deep Learning

Lingjing Kong, Tao LIN, Anastasia Koloskova, Martin Jaggi, Sebastian Stich

A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning

Abi Komanduru, Jean Honorio

One-sided Frank-Wolfe algorithms for saddle problems

Vladimir Kolmogorov, Thomas Pock

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang

Representational aspects of depth and conditioning in normalizing flows

Frederic Koehler, Viraj Mehta, Andrej Risteski

Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More

Johannes Gasteiger, Marten Lienen, Stephan Günnemann

CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients

Dani Kiyasseh, Tingting Zhu, David A Clifton

Bias-Robust Bayesian Optimization via Dueling Bandits

Johannes Kirschner, Andreas Krause

ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision

Wonjae Kim, Bokyung Son, Ildoo Kim

Unsupervised Skill Discovery with Bottleneck Option Learning

Jaekyeom Kim, Seohong Park, Gunhee Kim

The Lipschitz Constant of Self-Attention

Hyunjik Kim, George Papamakarios, Andriy Mnih

Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations

Timothy D. Kim, Thomas Z. Luo, Jonathan W. Pillow, Carlos D. Brody

A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning

Dong Ki Kim, Miao Liu, Matthew D Riemer, Chuangchuang Sun, Marwa Abdulhai

Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

Jaehyeon Kim, Jungil Kong, Juhee Son

Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning

Taehyeong Kim, Injune Hwang, Hyundo Lee, Hyunseo Kim, Won-Seok Choi

I-BERT: Integer-only BERT Quantization

Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer

Reward Identification in Inverse Reinforcement Learning

Kuno Kim, Shivam Garg, Kirankumar Shiragur, Stefano Ermon

Self-Improved Retrosynthetic Planning

Junsu Kim, Sungsoo Ahn, Hankook Lee, Jinwoo Shin

Improving Predictors via Combination Across Diverse Task Categories

Kwang In Kim

GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training

Krishnateja Killamsetty, Durga S, Ganesh Ramakrishnan, Abir De, Rishabh Iyer

Neural SDEs as Infinite-Dimensional GANs

Patrick Kidger, James Foster, Xuechen Li, Terry J Lyons

"Hey, that’s not an ODE": Faster ODE Adjoints via Seminorms

Patrick Kidger, Ricky T. Q. Chen, Terry J Lyons

Functional Space Analysis of Local GAN Convergence

Valentin Khrulkov, Artem Babenko, Ivan Oseledets

Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm

Sajad Khodadadian, Zaiwei Chen, Siva Theja Maguluri

Markpainting: Adversarial Machine Learning meets Inpainting

David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross Anderson

Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets

Thomas Kerdreux, Lewis Liu, Simon Lacoste-Julien, Damien Scieur

Interpretable Stability Bounds for Spectral Graph Filters

Henry Kenlay, Dorina Thanou, Xiaowen Dong

Self Normalizing Flows

Thomas A Keller, Jorn W.T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré

Prior Image-Constrained Reconstruction using Style-Based Generative Models

Varun A Kelkar, Mark Anastasio

Regularized Submodular Maximization at Scale

Ehsan Kazemi, Shervin Minaee, Moran Feldman, Amin Karbasi

When Does Data Augmentation Help With Membership Inference Attacks?

Yigitcan Kaya, Tudor Dumitras

Improved Algorithms for Agnostic Pool-based Active Classification

Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson

Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation

Masahiro Kato, Takeshi Teshima

Learning from History for Byzantine Robust Optimization

Sai Praneeth Karimireddy, Lie He, Martin Jaggi

Off-Policy Confidence Sequences

Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas

Variational Auto-Regressive Gaussian Processes for Continual Learning

Sanyam Kapoor, Theofanis Karaletsos, Thang D Bui