ICML 2022

1,233 papers

GraphFM: Improving Large-Scale GNN Training via Feature Momentum

Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang

The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks

Xin Yu, Thiago Serra, Srikumar Ramalingam, Shandian Zhe

Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning

Sixing Yu, Arya Mazaheri, Ali Jannesari

Reachability Constrained Reinforcement Learning

Dongjie Yu, Haitong Ma, Shengbo Li, Jianyu Chen

How to Leverage Unlabeled Data in Offline Reinforcement Learning

Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn

Understanding Robust Overfitting of Adversarial Training and Beyond

Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong

Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks

Zhaoning Yu, Hongyang Gao

ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks

Haoran You, Baopu Li, Shi Huihong, Yonggan Fu, Yingyan Lin

Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization

Jaehong Yoon, Geon Park, Wonyong Jeong, Sung Ju Hwang

Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations

Fan Yin, Jieying Jiao, Jun Yan, Guanyu Hu

De novo mass spectrometry peptide sequencing with a transformer model

Melih Yilmaz, William Fondrie, Wout Bittremieux, Sewoong Oh, William S Noble

QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning

Liping Yi, Wang Gang, Liu Xiaoguang

Be Like Water: Adaptive Floating Point for Machine Learning

Thomas Yeh, Max Sterner, Zerlina Lai, Brandon Chuang, Alexander Ihler

Centroid Approximation for Bootstrap: Improving Particle Quality at Inference

Mao Ye, Qiang Liu

Feature Space Particle Inference for Neural Network Ensembles

Shingo Yashima, Teppei Suzuki, Kohta Ishikawa, Ikuro Sato, Rei Kawakami

NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework

Xingcheng Yao, Yanan Zheng, Xiaocong Yang, Zhilin Yang

Improving Out-of-Distribution Robustness via Selective Augmentation

Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang

Learning from a Learning User for Optimal Recommendations

Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu

Identity-Disentangled Adversarial Augmentation for Self-supervised Learning

Kaiwen Yang, Tianyi Zhou, Xinmei Tian, Dacheng Tao

Anarchic Federated Learning

Haibo Yang, Xin Zhang, Prashant Khanduri, Jia Liu

A Study of Face Obfuscation in ImageNet

Kaiyu Yang, Jacqueline H. Yau, Li Fei-fei, Jia Deng, Olga Russakovsky

Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network

Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu

Fourier Learning with Cyclical Data

Yingxiang Yang, Zhihan Xiong, Tianyi Liu, Taiqing Wang, Chong Wang

A New Perspective on the Effects of Spectrum in Graph Neural Networks

Mingqi Yang, Yanming Shen, Rui Li, Heng Qi, Qiang Zhang

Linear Bandit Algorithms with Sublinear Time Complexity

Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon

Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity

Jianyi Yang, Shaolei Ren

Does the Data Induce Capacity Control in Deep Learning?

Rubing Yang, Jialin Mao, Pratik Chaudhari

Not All Poisons are Created Equal: Robust Training against Data Poisoning

Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman

Locally Sparse Neural Networks for Tabular Biomedical Data

Junchen Yang, Ofir Lindenbaum, Yuval Kluger

Injecting Logical Constraints into Neural Networks via Straight-Through Estimators

Zhun Yang, Joohyung Lee, Chiyoun Park

Efficient Variance Reduction for Meta-learning

Hansi Yang, James Kwok

Searching for BurgerFormer with Micro-Meso-Macro Space Design

Longxing Yang, YU HU, Shun Lu, Zihao Sun, Jilin Mei

Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion

Ling Yang, Shenda Hong

Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning

Ling Yang, Shenda Hong

A Psychological Theory of Explainability

Scott Cheng-Hsin Yang, Nils Erik Tomas Folke, Patrick Shafto

Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning

Shentao Yang, Yihao Feng, Shujian Zhang, Mingyuan Zhou

Self-Organized Polynomial-Time Coordination Graphs

Qianlan Yang, Weijun Dong, Zhizhou Ren, Jianhao Wang, Tonghan Wang

Active fairness auditing

Tom Yan, Chicheng Zhang

Optimally Controllable Perceptual Lossy Compression

Zeyu Yan, Fei Wen, Peilin Liu

Cycle Representation Learning for Inductive Relation Prediction

Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen

Diversified Adversarial Attacks based on Conjugate Gradient Method

Keiichiro Yamamura, Haruki Sato, Nariaki Tateiwa, Nozomi Hata, Toru Mitsutake

Investigating Why Contrastive Learning Benefits Robustness against Label Noise

Yihao Xue, Kyle Whitecross, Baharan Mirzasoleiman

Langevin Monte Carlo for Contextual Bandits

Pan Xu, Hongkai Zheng, Eric V Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar

A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization

Renzhe Xu, Xingxuan Zhang, Zheyan Shen, Tong Zhang, Peng Cui

Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization

Xiaojun Xu, Jacky Y Zhang, Evelyn Ma, Hyun Ho Son, Sanmi Koyejo

Adversarial Attack and Defense for Non-Parametric Two-Sample Tests

Xilie Xu, Jingfeng ZHANG, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli

Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations

Haoran Xu, Xianyuan Zhan, Honglei Yin, Huiling Qin

SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks

Xiang Xu, Karl D.D. Willis, Joseph G Lambourne, Chin-Yi Cheng, Pradeep Kumar Jayaraman

A Difference Standardization Method for Mutual Transfer Learning

Haoqing Xu, Meng Wang, Beilun Wang

On the Statistical Benefits of Curriculum Learning

Ziping Xu, Ambuj Tewari

Analyzing and Mitigating Interference in Neural Architecture Search

Jin Xu, Xu Tan, Kaitao Song, Renqian Luo, Yichong Leng

Prompting Decision Transformer for Few-Shot Policy Generalization

Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao

Inferring Cause and Effect in the Presence of Heteroscedastic Noise

Sascha Xu, Osman A Mian, Alexander Marx, Jilles Vreeken

Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces

Yinshuang Xu, Jiahui Lei, Edgar Dobriban, Kostas Daniilidis

Accurate Quantization of Measures via Interacting Particle-based Optimization

Lantian Xu, Anna Korba, Dejan Slepcev

Detached Error Feedback for Distributed SGD with Random Sparsification

An Xu, Heng Huang

Learning to Separate Voices by Spatial Regions

Alan Xu, Romit Roy Choudhury

Importance Weighted Kernel Bayes’ Rule

Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton

A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games

Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang

Efficient Computation of Higher-Order Subgraph Attribution via Message Passing

Ping Xiong, Thomas Schnake, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima

Self-Supervised Representation Learning via Latent Graph Prediction

Yaochen Xie, Zhao Xu, Shuiwang Ji

Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum

Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama

Robust Policy Learning over Multiple Uncertainty Sets

Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, Amy Zhang

COAT: Measuring Object Compositionality in Emergent Representations

Sirui Xie, Ari S Morcos, Song-chun Zhu, Ramakrishna Vedantam

Identification of Linear Non-Gaussian Latent Hierarchical Structure

Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng

Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm

Lechao Xiao, Jeffrey Pennington

ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning

Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, Stan Z. Li

Optimal Clustering with Noisy Queries via Multi-Armed Bandit

Jinghui Xia, zengfeng Huang

Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression

Jingfeng Wu, Difan Zou, Vladimir braverman, Quanquan Gu, Sham Kakade

RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval

Yihan Wu, Hongyang Zhang, Heng Huang

Nearly Optimal Policy Optimization with Stable at Any Time Guarantee

Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon Du

Flowformer: Linearizing Transformers with Conservation Flows

Haixu Wu, Jialong Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long

Revisiting Consistency Regularization for Deep Partial Label Learning

Dong-Dong Wu, Deng-Bao Wang, Min-ling Zhang

Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum

Junlin Wu, Yevgeniy Vorobeychik

DAVINZ: Data Valuation using Deep Neural Networks at Initialization

Zhaoxuan Wu, YAO SHU, Bryan Kian Hsiang Low

Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach

Shuang Wu, Ling Shi, Jun Wang, Guangjian Tian

Variational nearest neighbor Gaussian process

Luhuan Wu, Geoff Pleiss, John P Cunningham

Delay-Adaptive Step-sizes for Asynchronous Learning

Xuyang Wu, Sindri Magnússon, Hamid Reza Feyzmahdavian, Mikael Johansson

MemSR: Training Memory-efficient Lightweight Model for Image Super-Resolution

Kailu Wu, Chung-Kuei Lee, Kaisheng Ma

Instrumental Variable Regression with Confounder Balancing

Anpeng Wu, Kun Kuang, Bo Li, Fei Wu

Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks

Nan Wu, Stanislaw Jastrzebski, Kyunghyun Cho, Krzysztof J Geras

Self-supervised Models are Good Teaching Assistants for Vision Transformers

Haiyan Wu, Yuting Gao, Yinqi Zhang, Shaohui Lin, Yuan Xie

Structural Entropy Guided Graph Hierarchical Pooling

Junran Wu, Xueyuan Chen, Ke Xu, Shangzhe Li

Metric-Fair Classifier Derandomization

Jimmy Wu, Yatong Chen, Yang Liu

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes

Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time

David Woodruff, Amir Zandieh

A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications

Lukas Wolf, Ard Kastrati, Martyna B Plomecka, Jie-Ming Li, Dustin Klebe

Continual Learning with Guarantees via Weight Interval Constraints

Maciej Wołczyk, Karol Piczak, Bartosz Wójcik, Lukasz Pustelnik, Paweł Morawiecki

Easy Variational Inference for Categorical Models via an Independent Binary Approximation

Michael T Wojnowicz, Shuchin Aeron, Eric L Miller, Michael Hughes

Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning

Harley E Wiltzer, David Meger, Marc G. Bellemare

COLA: Consistent Learning with Opponent-Learning Awareness

Timon Willi, Alistair Hp Letcher, Johannes Treutlein, Jakob Foerster

Measure Estimation in the Barycentric Coding Model

Matthew Werenski, Ruijie Jiang, Abiy Tasissa, Shuchin Aeron, James M Murphy

Preconditioning for Scalable Gaussian Process Hyperparameter Optimization

Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John Cunningham, Jacob Gardner

Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming

Chuan Wen, Jianing Qian, Jierui Lin, Jiaye Teng, Dinesh Jayaraman

Random Forest Density Estimation

Hongwei Wen, Hanyuan Hang

BabelTower: Learning to Auto-parallelized Program Translation

Yuanbo Wen, Qi Guo, Qiang Fu, Xiaqing Li, Jianxing Xu

Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

Yuxin Wen, Jonas A. Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein

Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics

Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Kawahara Yoshinobu

Mitigating Neural Network Overconfidence with Logit Normalization

Hongxin Wei, RENCHUNZI XIE, Hao Cheng, Lei Feng, Bo An

Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets

Hongxin Wei, Lue Tao, RENCHUNZI XIE, Lei Feng, Bo An