ICML 2022

1,233 papers

Adapting to Mixing Time in Stochastic Optimization with Markovian Data

Ron Dorfman, Kfir Yehuda Levy

Fast rates for noisy interpolation require rethinking the effect of inductive bias

Konstantin Donhauser, Nicolò Ruggeri, Stefan Stojanovic, Fanny Yang

Privacy for Free: How does Dataset Condensation Help Privacy?

Tian Dong, Bo Zhao, Lingjuan Lyu

PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs

Zehao Dong, Muhan Zhang, Fuhai Li, Yixin Chen

Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks

Runpei Dong, Zhanhong Tan, Mengdi Wu, Linfeng Zhang, Kaisheng Ma

On the Adversarial Robustness of Causal Algorithmic Recourse

Ricardo Dominguez-Olmedo, Amir H Karimi, Bernhard Schölkopf

Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense

Bao Gia Doan, Ehsan M. Abbasnejad, Javen Qinfeng Shi, Damith C. Ranasinghe

Fair Generalized Linear Models with a Convex Penalty

Hyungrok Do, Preston Putzel, Axel S Martin, Padhraic Smyth, Judy Zhong

Generalization and Robustness Implications in Object-Centric Learning

Andrea Dittadi, Samuele S Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther

Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence

Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang, Mihailo Jovanovic

Understanding Doubly Stochastic Clustering

Tianjiao Ding, Derek Lim, René Vidal, Benjamin D Haeffele

Variational Feature Pyramid Networks

Panagiotis Dimitrakopoulos, Giorgos Sfikas, Christophoros Nikou

Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent

Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis

Streaming Algorithms for High-Dimensional Robust Statistics

Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas

Analysis of Stochastic Processes through Replay Buffers

Shirli Di-Castro, Shie Mannor, Dotan Di Castro

On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming

Gregory Dexter, Agniva Chowdhury, Haim Avron, Petros Drineas

Deep Causal Metric Learning

Xiang Deng, Zhongfei Zhang

NeuralEF: Deconstructing Kernels by Deep Neural Networks

Zhijie Deng, Jiaxin Shi, Jun Zhu

DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations

Fei Deng, Ingook Jang, Sungjin Ahn

Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass

Giorgia Dellaferrera, Gabriel Kreiman

Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization

Giuseppe Bruno De Luca, Eva Silverstein

Adversarial Vulnerability of Randomized Ensembles

Hassan Dbouk, Naresh Shanbhag

Attentional Meta-learners for Few-shot Polythetic Classification

Ben J Day, Ramon Viñas Torné, Nikola Simidjievski, Pietro Lió

Robust Multi-Objective Bayesian Optimization Under Input Noise

Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou

Distinguishing rule and exemplar-based generalization in learning systems

Ishita Dasgupta, Erin Grant, Tom Griffiths

Framework for Evaluating Faithfulness of Local Explanations

Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz

Knowledge Base Question Answering by Case-based Reasoning over Subgraphs

Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Manzil Zaheer

Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing

Mohammad Zalbagi Darestani, Jiayu Liu, Reinhard Heckel

Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems

Giannis Daras, Yuval Dagan, Alex Dimakis, Constantinos Daskalakis

Monarch: Expressive Structured Matrices for Efficient and Accurate Training

Tri Dao, Beidi Chen, Nimit S Sohoni, Arjun Desai, Michael Poli

Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation

Chris Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan

Unsupervised Image Representation Learning with Deep Latent Particles

Tal Daniel, Aviv Tamar

Understanding Robust Generalization in Learning Regular Languages

Soham Dan, Osbert Bastani, Dan Roth

Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning

Angelo Damiani, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli

Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization

Hanjun Dai, Mengjiao Yang, Yuan Xue, Dale Schuurmans, Bo Dai

DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training

Rong Dai, Li Shen, Fengxiang He, Xinmei Tian, Dacheng Tao

Dialog Inpainting: Turning Documents into Dialogs

Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Y Zhao, Aida Amini, Qazi Mamunur Rashid

Continuous Control with Action Quantization from Demonstrations

Robert Dadashi, Leonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk

Deep symbolic regression for recurrence prediction

Stéphane D’Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, Francois Charton

Principal Component Flows

Edmond Cunningham, Adam D Cobb, Susmit Jha

Only tails matter: Average-Case Universality and Robustness in the Convex Regime

Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette

Self-conditioning Pre-Trained Language Models

Xavier Suau Cuadros, Luca Zappella, Nicholas Apostoloff

Adversarial Robustness against Multiple and Single l_p-Threat Models via Quick Fine-Tuning of Robust Classifiers

Francesco Croce, Matthias Hein

Evaluating the Adversarial Robustness of Adaptive Test-time Defenses

Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein

Label-Free Explainability for Unsupervised Models

Jonathan Crabbe, Mihaela van der Schaar

Counterfactual Transportability: A Formal Approach

Juan D Correa, Sanghack Lee, Elias Bareinboim

Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model

Jean-Rémy Conti, Nathan Noiry, Stéphan Clémençon, Vincent Despiegel, Stéphane Gentric

Entropic Causal Inference: Graph Identifiability

Spencer Compton, Kristjan Greenewald, Dmitriy A Katz, Murat Kocaoglu

MAML and ANIL Provably Learn Representations

Liam Collins, Aryan Mokhtari, Sewoong Oh, Sanjay Shakkottai

Transfer and Marginalize: Explaining Away Label Noise with Privileged Information

Mark Collier, Rodolphe Jenatton, Effrosyni Kokiopoulou, Jesse Berent

One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams

Benjamin Coleman, Benito Geordie, Li Chou, R. A. Leo Elworth, Todd Treangen

Massively Parallel k-Means Clustering for Perturbation Resilient Instances

Vincent Cohen-Addad, Vahab Mirrokni, Peilin Zhong

Online and Consistent Correlation Clustering

Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis

Diffusion bridges vector quantized variational autoencoders

Max Cohen, Guillaume Quispe, Sylvain Le Corff, Charles Ollion, Eric Moulines

On the Robustness of CountSketch to Adaptive Inputs

Edith Cohen, Xin Lyu, Jelani Nelson, Tamas Sarlos, Moshe Shechner

Context-Aware Drift Detection

Oliver Cobb, Arnaud Van Looveren

Unified Scaling Laws for Routed Language Models

Aidan Clark, Diego de Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini

TPC: Transformation-Specific Smoothing for Point Cloud Models

Wenda Chu, Linyi Li, Bo Li

DNA: Domain Generalization with Diversified Neural Averaging

Xu Chu, Yujie Jin, Wenwu Zhu, Yasha Wang, Xin Wang

Shuffle Private Linear Contextual Bandits

Sayak Ray Chowdhury, Xingyu Zhou

From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers

Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish

Selective Network Linearization for Efficient Private Inference

Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, Chinmay Hegde

Discrete Probabilistic Inverse Optimal Transport

Wei-Ting Chiu, Pei Wang, Patrick Shafto

Self-supervised learning with random-projection quantizer for speech recognition

Chung-Cheng Chiu, James Qin, Yu Zhang, Jiahui Yu, Yonghui Wu

RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests

Victor Chernozhukov, Whitney Newey, Vı́ctor M Quintas-Martı́nez, Vasilis Syrgkanis

Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra

Nadiia Chepurko, Kenneth Clarkson, Lior Horesh, Honghao Lin, David Woodruff

Adversarially Trained Actor Critic for Offline Reinforcement Learning

Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal

Task-aware Privacy Preservation for Multi-dimensional Data

Jiangnan Cheng, Ao Tang, Sandeep Chinchali

Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis

Ziyi Chen, Yi Zhou, Rong-Rong Chen, Shaofeng Zou

Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation

Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, Liwei Wang

Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness

Tianlong Chen, Huan Zhang, Zhenyu Zhang, Shiyu Chang, Sijia Liu

Data-Efficient Double-Win Lottery Tickets from Robust Pre-training

Tianlong Chen, Zhenyu Zhang, Sijia Liu, Yang Zhang, Shiyu Chang

Efficient Online ML API Selection for Multi-Label Classification Tasks

Lingjiao Chen, Matei Zaharia, James Zou

State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks

Yanqi Chen, Zhaofei Yu, Wei Fang, Zhengyu Ma, Tiejun Huang

Adaptive Model Design for Markov Decision Process

Siyu Chen, Donglin Yang, Jiayang Li, Senmiao Wang, Zhuoran Yang

Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile

Dong Chen, Lingfei Wu, Siliang Tang, Xiao Yun, Bo Long

Optimization-Induced Graph Implicit Nonlinear Diffusion

Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin

Auxiliary Learning with Joint Task and Data Scheduling

Hong Chen, Xin Wang, Chaoyu Guan, Yue Liu, Wenwu Zhu

Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection

wenchao chen, Long Tian, Bo Chen, Liang Dai, Zhibin Duan

Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters

Xin Chen, Yujie Tang, Na Li

Faster Fundamental Graph Algorithms via Learned Predictions

Justin Chen, Sandeep Silwal, Ali Vakilian, Fred Zhang

On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation

Xiaohong Chen, Zhengling Qi

Learning Mixtures of Linear Dynamical Systems

Yanxi Chen, H. Vincent Poor

The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation

Wei-Ning Chen, Ayfer Ozgur, Peter Kairouz

Structure-Aware Transformer for Graph Representation Learning

Dexiong Chen, Leslie O’Bray, Karsten Borgwardt

Flow-based Recurrent Belief State Learning for POMDPs

Xiaoyu Chen, Yao Mark Mu, Ping Luo, Shengbo Li, Jianyu Chen

On Non-local Convergence Analysis of Deep Linear Networks

Kun Chen, Dachao Lin, Zhihua Zhang

Weisfeiler-Lehman Meets Gromov-Wasserstein

Samantha Chen, Sunhyuk Lim, Facundo Mémoli, Zhengchao Wan, Yusu Wang

ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases

Jintai Chen, Kuanlun Liao, Kun Wei, Haochao Ying, Danny Z Chen

Selling Data To a Machine Learner: Pricing via Costly Signaling

Junjie Chen, Minming Li, Haifeng Xu

Online Active Regression

Cheng Chen, Yi Li, Yiming Sun

On Collective Robustness of Bagging Against Data Poisoning

Ruoxin Chen, Zenan Li, Jie Li, Junchi Yan, Chentao Wu

Active Multi-Task Representation Learning

Yifang Chen, Kevin Jamieson, Simon Du

Learning Infinite-horizon Average-reward Markov Decision Process with Constraints

Liyu Chen, Rahul Jain, Haipeng Luo

Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP

Liyu Chen, Rahul Jain, Haipeng Luo

Streaming Algorithms for Support-Aware Histograms

Justin Chen, Piotr Indyk, Tal Wagner

On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs

Yuanzhou Chen, Jiafan He, Quanquan Gu

Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk

Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama

Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning

Mayee Chen, Daniel Y Fu, Avanika Narayan, Michael Zhang, Zhao Song

The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning

Wei-Ning Chen, Christopher A Choquette Choo, Peter Kairouz, Ananda Theertha Suresh