ICML 2022

1,233 papers

Training Your Sparse Neural Network Better with Any Mask

Ajay Kumar Jaiswal, Haoyu Ma, Tianlong Chen, Ying Ding, Zhangyang Wang

Combining Diverse Feature Priors

Saachi Jain, Dimitris Tsipras, Aleksander Madry

Biological Sequence Design with GFlowNets

Moksh Jain, Emmanuel Bengio, Alex Hernández-García, Jarrid Rector-Brooks, Bonaventure F. P. Dossou

Regret Minimization with Performative Feedback

Meena Jagadeesan, Tijana Zrnic, Celestine Mendler-Dünner

Input Dependent Sparse Gaussian Processes

Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernández-lobato

A deep convolutional neural network that is invariant to time rescaling

Brandon G Jacques, Zoran Tiganj, Aakash Sarkar, Marc Howard, Per Sederberg

Modeling Strong and Human-Like Gameplay with KL-Regularized Search

Athul Paul Jacob, David J Wu, Gabriele Farina, Adam Lerer, Hengyuan Hu

Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness

Shinji Ito

A Modern Self-Referential Weight Matrix That Learns to Modify Itself

Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber

The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention

Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber

LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation

David Ireland, Giovanni Montana

Bayesian Optimization for Distributionally Robust Chance-constrained Problem

Yu Inatsu, Shion Takeno, Masayuki Karasuyama, Ichiro Takeuchi

Parsimonious Learning-Augmented Caching

Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit

Datamodels: Understanding Predictions with Data and Data with Predictions

Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry

Inverse Contextual Bandits: Learning How Behavior Evolves over Time

Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar

Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization

Samuel Hurault, Arthur Leclaire, Nicolas Papadakis

A data-driven approach for learning to control computers

Peter C Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia

Robust Kernel Density Estimation with Median-of-Means principle

Pierre Humbert, Batiste Le Bars, Ludovic Minvielle

Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors

Geert-Jan Huizing, Laura Cantini, Gabriel Peyré

Directed Acyclic Transformer for Non-Autoregressive Machine Translation

Fei Huang, Hao Zhou, Yang Liu, Hang Li, Minlie Huang

Going Deeper into Permutation-Sensitive Graph Neural Networks

Zhongyu Huang, Yingheng Wang, Chaozhuo Li, Huiguang He

On the Learning of Non-Autoregressive Transformers

Fei Huang, Tianhua Tao, Hao Zhou, Lei Li, Minlie Huang

Efficient Representation Learning via Adaptive Context Pooling

Chen Huang, Walter Talbott, Navdeep Jaitly, Joshua M Susskind

Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology

Yan Huang, Ying Sun, Zehan Zhu, Changzhi Yan, Jinming Xu

SDQ: Stochastic Differentiable Quantization with Mixed Precision

Xijie Huang, Zhiqiang Shen, Shichao Li, Zechun Liu, Hu Xianghong

3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design

Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang

Action-Sufficient State Representation Learning for Control with Structural Constraints

Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour

Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)

Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, Longbo Huang

Frustratingly Easy Transferability Estimation

Long-Kai Huang, Junzhou Huang, Yu Rong, Qiang Yang, Ying Wei

Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits

Jiatai Huang, Yan Dai, Longbo Huang

Forward Operator Estimation in Generative Models with Kernel Transfer Operators

Zhichun Huang, Rudrasis Chakraborty, Vikas Singh

Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch

Transformer Quality in Linear Time

Weizhe Hua, Zihang Dai, Hanxiao Liu, Quoc Le

On the Role of Discount Factor in Offline Reinforcement Learning

Hao Hu, Yiqin Yang, Qianchuan Zhao, Chongjie Zhang

Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL

Siyi Hu, Chuanlong Xie, Xiaodan Liang, Xiaojun Chang

Neuron Dependency Graphs: A Causal Abstraction of Neural Networks

Yaojie Hu, Jin Tian

Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation

Pihe Hu, Yu Chen, Longbo Huang

Learning inverse folding from millions of predicted structures

Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie

Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling

Jiri Hron, Roman Novak, Jeffrey Pennington, Jascha Sohl-dickstein

AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

Wenzheng Hou, Qianqian Xu, Zhiyong Yang, Shilong Bao, Yuan He

Conditional GANs with Auxiliary Discriminative Classifier

Liang Hou, Qi Cao, Huawei Shen, Siyuan Pan, Xiaoshuang Li

Equivariant Diffusion for Molecule Generation in 3D

Emiel Hoogeboom, Vı́ctor Garcia Satorras, Clément Vignac, Max Welling

DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning

Robert Hönig, Yiren Zhao, Robert Mullins

Deep Hierarchy in Bandits

Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh

Neural Laplace: Learning diverse classes of differential equations in the Laplace domain

Samuel I Holt, Zhaozhi Qian, Mihaela van der Schaar

Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology

Valentin Hofmann, Janet Pierrehumbert, Hinrich Schütze

Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers

Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael Mahoney

Scaling Out-of-Distribution Detection for Real-World Settings

Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon

NOMU: Neural Optimization-based Model Uncertainty

Jakob M Heiss, Jakob Weissteiner, Hanna S Wutte, Sven Seuken, Josef Teichmann

Label-Descriptive Patterns and Their Application to Characterizing Classification Errors

Michael A. Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken

HyperPrompt: Prompt-based Task-Conditioning of Transformers

Yun He, Steven Zheng, Yi Tay, Jai Gupta, Yu Du

A Reduction from Linear Contextual Bandits Lower Bounds to Estimations Lower Bounds

Jiahao He, Jiheng Zhang, Rachel Q. Zhang

Sparse Double Descent: Where Network Pruning Aggravates Overfitting

Zheng He, Zeke Xie, Quanzhi Zhu, Zengchang Qin

Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning

Bobby He, Mete Ozay

GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks

Yixuan He, Quan Gan, David Wipf, Gesine D Reinert, Junchi Yan

On Distribution Shift in Learning-based Bug Detectors

Jingxuan He, Luca Beurer-Kellner, Martin Vechev

General-purpose, long-context autoregressive modeling with Perceiver AR

Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea, Sebastian Borgeaud, Charlie Nash

C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra

Yuka Hashimoto, Zhao Wang, Tomoko Matsui

Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses

Keegan Harris, Dung Daniel T Ngo, Logan Stapleton, Hoda Heidari, Steven Wu

Implicit Regularization with Polynomial Growth in Deep Tensor Factorization

Kais Hariz, Hachem Kadri, Stéphane Ayache, Maher Moakher, Thierry Artières

GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing

Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song

Contextual Information-Directed Sampling

Botao Hao, Tor Lattimore, Chao Qin

TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm

Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar

Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning

Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine

Temporal Difference Learning for Model Predictive Control

Nicklas A Hansen, Hao Su, Xiaolong Wang

Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification

Jun-Yi Hang, Min-ling Zhang

Stochastic Reweighted Gradient Descent

Ayoub El Hanchi, David Stephens, Chris Maddison

Random Gegenbauer Features for Scalable Kernel Methods

Insu Han, Amir Zandieh, Haim Avron

Adversarial Attacks on Gaussian Process Bandits

Eric Han, Jonathan Scarlett

Off-Policy Reinforcement Learning with Delayed Rewards

Beining Han, Zhizhou Ren, Zuofan Wu, Yuan Zhou, Jian Peng

Private Streaming SCO in ℓ_p geometry with Applications in High Dimensional Online Decision Making

Yuxuan Han, Zhicong Liang, Zhipeng Liang, Yang Wang, Yuan Yao

G-Mixup: Graph Data Augmentation for Graph Classification

Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu

Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes

Insu Han, Mike Gartrell, Elvis DOHMATOB, Amin Karbasi

You Only Cut Once: Boosting Data Augmentation with a Single Cut

Junlin Han, Pengfei Fang, Weihao Li, Jie Hong, Mohammad Ali Armin

Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets

Guy Hacohen, Avihu Dekel, Daphna Weinshall

NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks

Mustafa B Gurbuz, Constantine Dovrolis

Understanding and Improving Knowledge Graph Embedding for Entity Alignment

Lingbing Guo, Qiang Zhang, Zequn Sun, Mingyang Chen, Wei Hu

Fast Provably Robust Decision Trees and Boosting

Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou

Online Continual Learning through Mutual Information Maximization

Yiduo Guo, Bing Liu, Dongyan Zhao

Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage

Alan J.X. Guo, Cong Liang, Qing-Hu Hou

Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding

Lan-Zhe Guo, Yu-feng Li

Adversarially trained neural representations may already be as robust as corresponding biological neural representations

Chong Guo, Michael Lee, Guillaume Leclerc, Joel Dapello, Yug Rao

Bounding Training Data Reconstruction in Private (Deep) Learning

Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten

No-Regret Learning in Partially-Informed Auctions

Wenshuo Guo, Michael Jordan, Ellen Vitercik

Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes

Hongyi Guo, Qi Cai, Yufeng Zhang, Zhuoran Yang, Zhaoran Wang

A Parametric Class of Approximate Gradient Updates for Policy Optimization

Ramki Gummadi, Saurabh Kumar, Junfeng Wen, Dale Schuurmans

Identifiability Conditions for Domain Adaptation

Ishaan Gulrajani, Tatsunori Hashimoto

Large-Scale Graph Neural Architecture Search

Chaoyu Guan, Xin Wang, Hong Chen, Ziwei Zhang, Wenwu Zhu

Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity

Lin Guan, Sarath Sreedharan, Subbarao Kambhampati

Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning

Jiechao Guan, Zhiwu Lu

NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields

Shanyan Guan, Huayu Deng, Yunbo Wang, Xiaokang Yang

Learning Pseudometric-based Action Representations for Offline Reinforcement Learning

Pengjie Gu, Mengchen Zhao, Chen Chen, Dong Li, Jianye Hao

Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics

Yichen Gu, David T Blaauw, Joshua Welch

Adapting k-means Algorithms for Outliers

Christoph Grunau, Vaclav Rozhon

Mirror Learning: A Unifying Framework of Policy Optimisation

Jakub Grudzien, Christian A Schroeder De Witt, Jakob Foerster

Causal Inference Through the Structural Causal Marginal Problem

Luigi Gresele, Julius von Kügelgen, Jonas Kübler, Elke Kirschbaum, Bernhard Schölkopf

The State of Sparse Training in Deep Reinforcement Learning

Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro

Retrieval-Augmented Reinforcement Learning

Anirudh Goyal, Abram Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke

Secure Distributed Training at Scale

Eduard Gorbunov, Alexander Borzunov, Michael Diskin, Max Ryabinin

Partial Label Learning via Label Influence Function

Xiuwen Gong, Dong Yuan, Wei Bao