ICML 2021

1,183 papers

Finding Relevant Information via a Discrete Fourier Expansion

Mohsen Heidari, Jithin Sreedharan, Gil I Shamir, Wojciech Szpankowski

Logarithmic Regret for Reinforcement Learning with Linear Function Approximation

Jiafan He, Dongruo Zhou, Quanquan Gu

SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform

Yuhang He, Niki Trigoni, Andrew Markham

PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models

Chaoyang He, Shen Li, Mahdi Soltanolkotabi, Salman Avestimehr

Boosting for Online Convex Optimization

Elad Hazan, Karan Singh

SPECTRE: defending against backdoor attacks using robust statistics

Jonathan Hayase, Weihao Kong, Raghav Somani, Sewoong Oh

Hierarchical VAEs Know What They Don’t Know

Jakob D. Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe

Model Performance Scaling with Multiple Data Sources

Tatsunori Hashimoto

Valid Causal Inference with (Some) Invalid Instruments

Jason S Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-brown

Compressed Maximum Likelihood

Yi Hao, Alon Orlitsky

Bootstrapping Fitted Q-Evaluation for Off-Policy Inference

Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvári

Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient

Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang

Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning

Austin W. Hanjie, Victor Y Zhong, Karthik Narasimhan

A Collective Learning Framework to Boost GNN Expressiveness for Node Classification

Mengyue Hang, Jennifer Neville, Bruno Ribeiro

Adversarial Combinatorial Bandits with General Non-linear Reward Functions

Yanjun Han, Yining Wang, Xi Chen

Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration

Seungyul Han, Youngchul Sung

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher

Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning

Hassan Hafez-Kolahi, Behrad Moniri, Shohreh Kasaei, Mahdieh Soleymani Baghshah

Adapting to Delays and Data in Adversarial Multi-Armed Bandits

András György, Pooria Joulani

Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks

Nezihe Merve Gürel, Xiangyu Qi, Luka Rimanic, Ce Zhang, Bo Li

The Heavy-Tail Phenomenon in SGD

Mert Gurbuzbalaban, Umut Simsekli, Lingjiong Zhu

Correcting Exposure Bias for Link Recommendation

Shantanu Gupta, Hao Wang, Zachary Lipton, Yuyang Wang

Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting

Chirag Gupta, Aaditya Ramdas

UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning

Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Boehmer, Shimon Whiteson

Soft then Hard: Rethinking the Quantization in Neural Image Compression

Zongyu Guo, Zhizheng Zhang, Runsen Feng, Zhibo Chen

Adversarial Policy Learning in Two-player Competitive Games

Wenbo Guo, Xian Wu, Sui Huang, Xinyu Xing

Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games

Hongyi Guo, Zuyue Fu, Zhuoran Yang, Zhaoran Wang

On a Combination of Alternating Minimization and Nesterov’s Momentum

Sergey Guminov, Pavel Dvurechensky, Nazarii Tupitsa, Alexander Gasnikov

Operationalizing Complex Causes: A Pragmatic View of Mediation

Limor Gultchin, David Watson, Matt Kusner, Ricardo Silva

AutoAttend: Automated Attention Representation Search

Chaoyu Guan, Xin Wang, Wenwu Zhu

Crystallization Learning with the Delaunay Triangulation

Jiaqi Gu, Guosheng Yin

Detecting Rewards Deterioration in Episodic Reinforcement Learning

Ido Greenberg, Shie Mannor

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions

Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris Maddison

Dissecting Supervised Contrastive Learning

Florian Graf, Christoph Hofer, Marc Niethammer, Roland Kwitt

Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline

Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, Jia Deng

Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures

Martijn M Gösgens, Alexey Tikhonov, Liudmila Prokhorenkova

MARINA: Faster Non-Convex Distributed Learning with Compression

Eduard Gorbunov, Konstantin P. Burlachenko, Zhize Li, Peter Richtarik

On the Problem of Underranking in Group-Fair Ranking

Sruthi Gorantla, Amit Deshpande, Anand Louis

Active Slices for Sliced Stein Discrepancy

Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato

Function Contrastive Learning of Transferable Meta-Representations

Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer, Manuel Wuthrich

12-Lead ECG Reconstruction via Koopman Operators

Tomer Golany, Kira Radinsky, Daniel Freedman, Saar Minha

Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective

Florin Gogianu, Tudor Berariu, Mihaela C Rosca, Claudia Clopath, Lucian Busoniu

Query Complexity of Adversarial Attacks

Grzegorz Gluch, Rüdiger Urbanke

Differentially Private Quantiles

Jennifer Gillenwater, Matthew Joseph, Alex Kulesza

The Power of Adaptivity for Stochastic Submodular Cover

Rohan Ghuge, Anupam Gupta, viswanath nagarajan

Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message

Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha

EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL

Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu

Strategic Classification in the Dark

Ganesh Ghalme, Vineet Nair, Itay Eilat, Inbal Talgam-Cohen, Nir Rosenfeld

How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference

Amanda M Gentzel, Purva Pruthi, David Jensen

On the difficulty of unbiased alpha divergence minimization

Tomas Geffner, Justin Domke

Let’s Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework

Floris Geerts, Filip Mazowiecki, Guillermo Pérez

Parametric Graph for Unimodal Ranking Bandit

Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont, Boammani Aser Lompo

What does LIME really see in images?

Damien Garreau, Dina Mardaoui

On Proximal Policy Optimization’s Heavy-tailed Gradients

Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, Zico Kolter

RATT: Leveraging Unlabeled Data to Guarantee Generalization

Saurabh Garg, Sivaraman Balakrishnan, Zico Kolter, Zachary Lipton

Discriminative Complementary-Label Learning with Weighted Loss

Yi Gao, Min-ling Zhang

Unsupervised Co-part Segmentation through Assembly

Qingzhe Gao, Bin Wang, Libin Liu, Baoquan Chen

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks

Ruize Gao, Feng Liu, Jingfeng ZHANG, Bo Han, Tongliang Liu

An Information-Geometric Distance on the Space of Tasks

Yansong Gao, Pratik Chaudhari

Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine

Learning disentangled representations via product manifold projection

Marco Fumero, Luca Cosmo, Simone Melzi, Emanuele Rodolà

A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation

Scott Fujimoto, David Meger, Doina Precup

Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators

Yonggan Fu, Yongan Zhang, Yang Zhang, David Cox, Yingyan Lin

Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference

Yonggan Fu, Qixuan Yu, Meng Li, Vikas Chandra, Yingyan Lin

Learning Task Informed Abstractions

Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola

Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing

Cheng Fu, Hanxian Huang, Xinyun Chen, Yuandong Tian, Jishen Zhao

Bayesian Quadrature on Riemannian Data Manifolds

Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis

Variational Data Assimilation with a Learned Inverse Observation Operator

Thomas Frerix, Dmitrii Kochkov, Jamie Smith, Daniel Cremers, Michael Brenner

Post-selection inference with HSIC-Lasso

Tobias Freidling, Benjamin Poignard, Héctor Climente-González, Makoto Yamada

Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise

Spencer Frei, Yuan Cao, Quanquan Gu

Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins

Spencer Frei, Yuan Cao, Quanquan Gu

Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning

Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi

Efficient Online Learning for Dynamic k-Clustering

Dimitris Fotakis, Georgios Piliouras, Stratis Skoulakis

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design

Adam Foster, Desi R Ivanova, ILYAS MALIK, Tom Rainforth

Online A-Optimal Design and Active Linear Regression

Xavier Fontaine, Pierre Perrault, Michal Valko, Vianney Perchet

Online Learning with Optimism and Delay

Genevieve E Flaspohler, Francesco Orabona, Judah Cohen, Soukayna Mouatadid, Miruna Oprescu

What’s in the Box? Exploring the Inner Life of Neural Networks with Robust Rules

Jonas Fischer, Anna Olah, Jilles Vreeken

Scalable Certified Segmentation via Randomized Smoothing

Marc Fischer, Maximilian Baader, Martin Vechev

Few-Shot Conformal Prediction with Auxiliary Tasks

Adam Fisch, Tal Schuster, Tommi Jaakkola, Dr.Regina Barzilay

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups

Marc Finzi, Max Welling, Andrew Gordon Wilson

PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning

Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques

GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings

Matthias Fey, Jan E. Lenssen, Frank Weichert, Jure Leskovec

Understanding Noise Injection in GANs

Ruili Feng, Deli Zhao, Zheng-Jun Zha

KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation

Haozhe Feng, Zhaoyang You, Minghao Chen, Tianye Zhang, Minfeng Zhu

Provably Correct Optimization and Exploration with Non-linear Policies

Fei Feng, Wotao Yin, Alekh Agarwal, Lin Yang

Pointwise Binary Classification with Pairwise Confidence Comparisons

Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu

Uncertainty Principles of Encoding GANs

Ruili Feng, Zhouchen Lin, Jiapeng Zhu, Deli Zhao, Jingren Zhou

Reserve Price Optimization for First Price Auctions in Display Advertising

Zhe Feng, Sebastien Lahaie, Jon Schneider, Jinchao Ye

Dimensionality Reduction for the Sum-of-Distances Metric

Zhili Feng, Praneeth Kacham, David Woodruff

Lossless Compression of Efficient Private Local Randomizers

Vitaly Feldman, Kunal Talwar

Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach

Yingjie Fei, Zhuoran Yang, Zhaoran Wang

Unbalanced minibatch Optimal Transport; applications to Domain Adaptation

Kilian Fatras, Thibault Sejourne, Rémi Flamary, Nicolas Courty

Train simultaneously, generalize better: Stability of gradient-based minimax learners

Farzan Farnia, Asuman Ozdaglar

Connecting Optimal Ex-Ante Collusion in Teams to Extensive-Form Correlation: Faster Algorithms and Positive Complexity Results

Gabriele Farina, Andrea Celli, Nicola Gatti, Tuomas Sandholm

Near-Optimal Entrywise Anomaly Detection for Low-Rank Matrices with Sub-Exponential Noise

Vivek Farias, Andrew A Li, Tianyi Peng

PID Accelerated Value Iteration Algorithm

Amir-massoud Farahmand, Mohammad Ghavamzadeh

Streaming Bayesian Deep Tensor Factorization

Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, Shandian Zhe

Learning Bounds for Open-Set Learning

Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang

On Variational Inference in Biclustering Models

Guanhua Fang, Ping Li

On Estimation in Latent Variable Models

Guanhua Fang, Ping Li