ICLR 2022

1,094 papers

Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations

Weiqi Peng, Jinghui Chen

A global convergence theory for deep ReLU implicit networks via over-parameterization

Tianxiang Gao, Hailiang Liu, Jia Liu, Hridesh Rajan, Hongyang Gao

When, Why, and Which Pretrained GANs Are Useful?

Timofey Grigoryev, Andrey Voynov, Artem Babenko

FastSHAP: Real-Time Shapley Value Estimation

Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-in Lee, Rajesh Ranganath

Model Agnostic Interpretability for Multiple Instance Learning

Joseph Early, Christine Evers, Sarvapali Ramchurn

CrowdPlay: Crowdsourcing Human Demonstrations for Offline Learning

Matthias Gerstgrasser, Rakshit Trivedi, David C. Parkes

Monotonic Differentiable Sorting Networks

Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen

Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization

Biao Zhang, Peter Wonka

Mention Memory: incorporating textual knowledge into Transformers through entity mention attention

Michiel De Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Fei Sha, William W. Cohen

Explainable GNN-Based Models over Knowledge Graphs

David Jaime Tena Cucala, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik

The Evolution of Uncertainty of Learning in Games

Yun Kuen Cheung, Georgios Piliouras, Yixin Tao

FILM: Following Instructions in Language with Modular Methods

So Yeon Min, Devendra Singh Chaplot, Pradeep Kumar Ravikumar, Yonatan Bisk, Ruslan Salakhutdinov

Graph-Guided Network for Irregularly Sampled Multivariate Time Series

Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, Marinka Zitnik

One After Another: Learning Incremental Skills for a Changing World

Nur Muhammad Mahi Shafiullah, Lerrel Pinto

Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness

Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann

PAC Prediction Sets Under Covariate Shift

Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani

Object Pursuit: Building a Space of Objects via Discriminative Weight Generation

Chuanyu Pan, Yanchao Yang, Kaichun Mo, Yueqi Duan, Leonidas Guibas

Transformer-based Transform Coding

Yinhao Zhu, Yang Yang, Taco Cohen

Unified Visual Transformer Compression

Shixing Yu, Tianlong Chen, Jiayi Shen, Huan Yuan, Jianchao Tan

Consistent Counterfactuals for Deep Models

Emily Black, Zifan Wang, Matt Fredrikson

Neural Methods for Logical Reasoning over Knowledge Graphs

Alfonso Amayuelas, SHUAI ZHANG, Xi Susie Rao, Ce Zhang

Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations

Keir Adams, Lagnajit Pattanaik, Connor W. Coley

Conditional Image Generation by Conditioning Variational Auto-Encoders

William Harvey, Saeid Naderiparizi, Frank Wood

Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks

Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Mahmut Kandemir, Anand Sivasubramaniam

Effect of scale on catastrophic forgetting in neural networks

Vinay Venkatesh Ramasesh, Aitor Lewkowycz, Ethan Dyer

On the benefits of maximum likelihood estimation for Regression and Forecasting

Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh

TRAIL: Near-Optimal Imitation Learning with Suboptimal Data

Mengjiao Yang, Sergey Levine, Ofir Nachum

Efficient Computation of Deep Nonlinear Infinite-Width Neural Networks that Learn Features

Greg Yang, Michael Santacroce, Edward J Hu

Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective

Luca Scimeca, Seong Joon Oh, Sanghyuk Chun, Michael Poli, Sangdoo Yun

LoRA: Low-Rank Adaptation of Large Language Models

Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li

Hindsight Foresight Relabeling for Meta-Reinforcement Learning

Michael Wan, Jian Peng, Tanmay Gangwani

Step-unrolled Denoising Autoencoders for Text Generation

Nikolay Savinov, Junyoung Chung, Mikolaj Binkowski, Erich Elsen, Aaron van den Oord

Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming

Sachin G Konan, Esmaeil Seraj, Matthew Gombolay

P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts

Benjamin Newman, Prafulla Kumar Choubey, Nazneen Rajani

Differentially Private Fine-tuning of Language Models

Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan

A Fine-Tuning Approach to Belief State Modeling

Samuel Sokota, Hengyuan Hu, David J Wu, J Zico Kolter, Jakob Nicolaus Foerster

An Agnostic Approach to Federated Learning with Class Imbalance

Zebang Shen, Juan Cerviño, Hamed Hassani, Alejandro Ribeiro

Optimizer Amalgamation

Tianshu Huang, Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini

Curriculum learning as a tool to uncover learning principles in the brain

Daniel R. Kepple, Rainer Engelken, Kanaka Rajan

Learning to Dequantise with Truncated Flows

Shawn Tan, Chin-Wei Huang, Alessandro Sordoni, Aaron Courville

How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis

SHUAI ZHANG, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong

Predicting Physics in Mesh-reduced Space with Temporal Attention

Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, Liping Liu

Model Zoo: A Growing Brain That Learns Continually

Rahul Ramesh, Pratik Chaudhari

Path Integral Sampler: A Stochastic Control Approach For Sampling

Qinsheng Zhang, Yongxin Chen

Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations

Fangyu Liu, Yunlong Jiao, Jordan Massiah, Emine Yilmaz, Serhii Havrylov

T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis

Minhao LIU, Ailing Zeng, Qiuxia LAI, Ruiyuan Gao, Min Li

Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity

Lu Mi, Richard Xu, Sridhama Prakhya, Albert Lin, Nir Shavit

Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective

Wei Huang, Yayong Li, weitao Du, Richard Xu, Jie Yin

Energy-Inspired Molecular Conformation Optimization

Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma

Granger causal inference on DAGs identifies genomic loci regulating transcription

Alexander P Wu, Rohit Singh, Bonnie Berger

ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning

Debasmit Das, Sungrack Yun, Fatih Porikli

Conditional Contrastive Learning with Kernel

Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao, Kun Zhang

Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop

Peng Jin, Xitong Zhang, Yinpeng Chen, Sharon X Huang, Zicheng Liu

BAM: Bayes with Adaptive Memory

Josue Nassar, Jennifer Rogers Brennan, Ben Evans, Kendall Lowrey

An Experimental Design Perspective on Model-Based Reinforcement Learning

Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger

Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression

Bae Seong Park, Se Jung Kwon, Daehwan Oh, Byeongwook Kim, Dongsoo Lee

Learning Weakly-supervised Contrastive Representations

Yao-Hung Hubert Tsai, Tianqin Li, Weixin Liu, Peiyuan Liao, Ruslan Salakhutdinov

Tuformer: Data-driven Design of Transformers for Improved Generalization or Efficiency

Xiaoyu Liu, Jiahao Su, Furong Huang

Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?

Matthew Farrell, Blake Bordelon, Shubhendu Trivedi, Cengiz Pehlevan

An Operator Theoretic View On Pruning Deep Neural Networks

William T Redman, Maria Fonoberova, Ryan Mohr, Yannis Kevrekidis, Igor Mezić

Learning Object-Oriented Dynamics for Planning from Text

Guiliang Liu, Ashutosh Adhikari, Amir-massoud Farahmand, Pascal Poupart

Neural Networks as Kernel Learners: The Silent Alignment Effect

Alexander Atanasov, Blake Bordelon, Cengiz Pehlevan

When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?

Ziang Song, Song Mei, Yu Bai

The Three Stages of Learning Dynamics in High-dimensional Kernel Methods

Nikhil Ghosh, Song Mei, Bin Yu

On feature learning in neural networks with global convergence guarantees

Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna

GDA-AM: ON THE EFFECTIVENESS OF SOLVING MIN-IMAX OPTIMIZATION VIA ANDERSON MIXING

Huan He, Shifan Zhao, Yuanzhe Xi, Joyce Ho, Yousef Saad

Training Transition Policies via Distribution Matching for Complex Tasks

Ju-Seung Byun, Andrew Perrault

DictFormer: Tiny Transformer with Shared Dictionary

Qian Lou, Ting Hua, Yen-Chang Hsu, Yilin Shen, Hongxia Jin

switch-GLAT: Multilingual Parallel Machine Translation Via Code-Switch Decoder

Zhenqiao Song, Hao Zhou, Lihua Qian, Jingjing Xu, Shanbo Cheng

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu

Generalizing Few-Shot NAS with Gradient Matching

Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-jui Hsieh

Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation

Julius Adebayo, Michael Muelly, Harold Abelson, Been Kim

SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu

No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models

Chen Liang, Haoming Jiang, Simiao Zuo, Pengcheng He, Xiaodong Liu

Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias

Frederic Koehler, Viraj Mehta, Chenghui Zhou, Andrej Risteski

Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers

Guodong Zhang, Aleksandar Botev, James Martens

On Redundancy and Diversity in Cell-based Neural Architecture Search

Xingchen Wan, Binxin Ru, Pedro M Esperanca, Zhenguo Li

An Information Fusion Approach to Learning with Instance-Dependent Label Noise

Zhimeng Jiang, Kaixiong Zhou, Zirui Liu, Li Li, Rui Chen

ComPhy: Compositional Physical Reasoning of Objects and Events from Videos

Zhenfang Chen, Kexin Yi, Yunzhu Li, Mingyu Ding, Antonio Torralba

Cross-Lingual Transfer with Class-Weighted Language-Invariant Representations

Ruicheng Xian, Heng Ji, Han Zhao

FairCal: Fairness Calibration for Face Verification

Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall, Adam M Oberman

Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction

Mingyue Tang, Pan Li, Carl Yang

Towards Understanding Generalization via Decomposing Excess Risk Dynamics

Jiaye Teng, Jianhao Ma, Yang Yuan

The Close Relationship Between Contrastive Learning and Meta-Learning

Renkun Ni, Manli Shu, Hossein Souri, Micah Goldblum, Tom Goldstein

Policy Smoothing for Provably Robust Reinforcement Learning

Aounon Kumar, Alexander Levine, Soheil Feizi

Zero-Shot Self-Supervised Learning for MRI Reconstruction

Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Mehmet Akcakaya

Graph Neural Networks with Learnable Structural and Positional Representations

Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson

Maximizing Ensemble Diversity in Deep Reinforcement Learning

Hassam Sheikh, Mariano Phielipp, Ladislau Boloni

Adversarial Unlearning of Backdoors via Implicit Hypergradient

Yi Zeng, Si Chen, Won Park, Zhuoqing Mao, Ming Jin

Learning Neural Contextual Bandits through Perturbed Rewards

Yiling Jia, Weitong ZHANG, Dongruo Zhou, Quanquan Gu, Hongning Wang

PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication

Cheng Wan, Youjie Li, Cameron R. Wolfe, Anastasios Kyrillidis, Nam Sung Kim

Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems

Hadi Abdullah, Aditya Karlekar, Vincent Bindschaedler, Patrick Traynor

Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect

Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao

How Much Can CLIP Benefit Vision-and-Language Tasks?

Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach

Generalized Kernel Thinning

Raaz Dwivedi, Lester Mackey

No One Representation to Rule Them All: Overlapping Features of Training Methods

Raphael Gontijo-Lopes, Yann Dauphin, Ekin Dogus Cubuk

Bi-linear Value Networks for Multi-goal Reinforcement Learning

Zhang-Wei Hong, Ge Yang, Pulkit Agrawal

Revisit Kernel Pruning with Lottery Regulated Grouped Convolutions

Shaochen Zhong, Guanqun Zhang, Ningjia Huang, Shuai Xu

Graph-Relational Domain Adaptation

Zihao Xu, Hao He, Guang-He Lee, Bernie Wang, Hao Wang

Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning

Mark Hamilton, Scott Lundberg, Stephanie Fu, Lei Zhang, William T. Freeman