ICLR 2023

1,573 papers

Edge Guided GANs with Contrastive Learning for Semantic Image Synthesis

Hao Tang, Xiaojuan Qi, Guolei Sun, Dan Xu, Nicu Sebe

HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork

Jae Yong Lee, SungWoong CHO, Hyung Ju Hwang

Pseudo-label Training and Model Inertia in Neural Machine Translation

Benjamin Hsu, Anna Currey, Xing Niu, Maria Nǎdejde, Georgiana Dinu

How robust is unsupervised representation learning to distribution shift?

Yuge Shi, Imant Daunhawer, Julia E Vogt, Philip Torr, Amartya Sanyal

Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse

Martin Pawelczyk, Teresa Datta, Johan Van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju

A Self-Attention Ansatz for Ab-initio Quantum Chemistry

Ingrid von Glehn, James S Spencer, David Pfau

AE-FLOW: Autoencoders with Normalizing Flows for Medical Images Anomaly Detection

Yuzhong Zhao, Qiaoqiao Ding, Xiaoqun Zhang

Computing all Optimal Partial Transports

Abhijeet Phatak, Sharath Raghvendra, CHITTARANJAN TRIPATHY, Kaiyi Zhang

BALTO: fast tensor program optimization with diversity-based active learning

Jun Bi, Xiaqing Li, Qi Guo, Rui Zhang, Yuanbo Wen

Learning What and Where: Disentangling Location and Identity Tracking Without Supervision

Manuel Traub, Sebastian Otte, Tobias Menge, Matthias Karlbauer, Jannik Thuemmel

On the Trade-Off between Actionable Explanations and the Right to be Forgotten

Martin Pawelczyk, Tobias Leemann, Asia Biega, Gjergji Kasneci

A Theory of Dynamic Benchmarks

Ali Shirali, Rediet Abebe, Moritz Hardt

EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data

Michael Crawshaw, Yajie Bao, Mingrui Liu

Bidirectional Language Models Are Also Few-shot Learners

Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli, Noah Constant, Colin Raffel

Energy-Based Test Sample Adaptation for Domain Generalization

Zehao Xiao, Xiantong Zhen, Shengcai Liao, Cees G. M. Snoek

Learning Rationalizable Equilibria in Multiplayer Games

Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin

HypeR: Multitask Hyper-Prompted Training Enables Large-Scale Retrieval Generalization

Zefeng Cai, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng

Online Boundary-Free Continual Learning by Scheduled Data Prior

Hyunseo Koh, Minhyuk Seo, Jihwan Bang, Hwanjun Song, Deokki Hong

Achieving Near-Optimal Individual Regret & Low Communications in Multi-Agent Bandits

Xuchuang Wang, Lin Yang, Yu-Zhen Janice Chen, Xutong Liu, Mohammad Hajiesmaili

Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

Zhendong Wang, Jonathan J Hunt, Mingyuan Zhou

Learning to Induce Causal Structure

Nan Rosemary Ke, Silvia Chiappa, Jane X Wang, Jörg Bornschein, Anirudh Goyal

Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics

Sirui Zheng, Lingxiao Wang, Shuang Qiu, Zuyue Fu, Zhuoran Yang

Stay Moral and Explore: Learn to Behave Morally in Text-based Games

Zijing Shi, Meng Fang, Yunqiu Xu, Ling Chen, Yali Du

Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism

Zhijian Zhuo, Yifei Wang, Jinwen Ma, Yisen Wang

Bridging the Gap to Real-World Object-Centric Learning

Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao

Strong inductive biases provably prevent harmless interpolation

Michael Aerni, Marco Milanta, Konstantin Donhauser, Fanny Yang

Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping

Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni

Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning

Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck, Josiah P. Hanna, Stefano V Albrecht

Understanding The Robustness of Self-supervised Learning Through Topic Modeling

Zeping Luo, Shiyou Wu, Cindy Weng, Mo Zhou, Rong Ge

FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data

Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley

kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference

Benfeng Xu, Quan Wang, Zhendong Mao, Yajuan Lyu, Qiaoqiao She

Autoregressive Conditional Neural Processes

Wessel Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom Andersson

Robust Scheduling with GFlowNets

David W Zhang, Corrado Rainone, Markus Peschl, Roberto Bondesan

Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games

Wenhao Zhan, Jason D. Lee, Zhuoran Yang

PAC Reinforcement Learning for Predictive State Representations

Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee

MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection

Jiaxun Cui, Xiaomeng Yang, Mulong Luo, Geunbae Lee, Peter Stone

O-GNN: incorporating ring priors into molecular modeling

Jinhua Zhu, Kehan Wu, Bohan Wang, Yingce Xia, Shufang Xie

Latent Neural ODEs with Sparse Bayesian Multiple Shooting

Valerii Iakovlev, Cagatay Yildiz, Markus Heinonen, Harri Lahdesmaki

Understanding DDPM Latent Codes Through Optimal Transport

Valentin Khrulkov, Gleb Ryzhakov, Andrei Chertkov, Ivan Oseledets

Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation

David Lipshutz, Cengiz Pehlevan, Dmitri Chklovskii

Generating Sequences by Learning to Self-Correct

Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen

Disentanglement of Correlated Factors via Hausdorff Factorized Support

Karsten Roth, Mark Ibrahim, Zeynep Akata, Pascal Vincent, Diane Bouchacourt

TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation

Hyesu Lim, Byeonggeun Kim, Jaegul Choo, Sungha Choi

Sequential Gradient Coding For Straggler Mitigation

Nikhil Krishnan Muralee Krishnan, MohammadReza Ebrahimi, Ashish J Khisti

Revisiting Populations in multi-agent Communication

Paul Michel, Mathieu Rita, Kory Wallace Mathewson, Olivier Tieleman, Angeliki Lazaridou

Amortised Invariance Learning for Contrastive Self-Supervision

Ruchika Chavhan, Jan Stuehmer, Calum Heggan, Mehrdad Yaghoobi, Timothy Hospedales

Neural Implicit Shape Editing using Boundary Sensitivity

Arturs Berzins, Moritz Ibing, Leif Kobbelt

Improved Learning-augmented Algorithms for k-means and k-medians Clustering

Thy Dinh Nguyen, Anamay Chaturvedi, Huy Nguyen

SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments

Tsun-Hsuan Wang, Pingchuan Ma, Andrew Everett Spielberg, Zhou Xian, Hao Zhang

Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning

Ivona Najdenkoska, Xiantong Zhen, Marcel Worring

Scaling Laws For Deep Learning Based Image Reconstruction

Tobit Klug, Reinhard Heckel

In-Situ Text-Only Adaptation of Speech Models with Low-Overhead Speech Imputations

Ashish Mittal, Sunita Sarawagi, Preethi Jyothi

MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises

Emanuele Palumbo, Imant Daunhawer, Julia E Vogt

Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles

Biswadeep Chakraborty, Saibal Mukhopadhyay

(Certified!!) Adversarial Robustness for Free!

Nicholas Carlini, Florian Tramer, Krishnamurthy Dj Dvijotham, Leslie Rice, Mingjie Sun

HiT-MDP: Learning the SMDP option framework on MDPs with Hidden Temporal Embeddings

Chang Li, Dongjin Song, Dacheng Tao

Making Better Decision by Directly Planning in Continuous Control

Jinhua Zhu, Yue Wang, Lijun Wu, Tao Qin, Wengang Zhou

SP2 : A Second Order Stochastic Polyak Method

Shuang Li, William Joseph Swartworth, Martin Takac, Deanna Needell, Robert M. Gower

On the Convergence of AdaGrad(Norm) on R^d: Beyond Convexity, Non-Asymptotic Rate and Acceleration

Zijian Liu, Ta Duy Nguyen, Alina Ene, Huy Nguyen

Order Matters: Agent-by-agent Policy Optimization

Xihuai Wang, Zheng Tian, Ziyu Wan, Ying Wen, Jun Wang

Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time

Jun-Kun Wang, Andre Wibisono

Equivariance-aware Architectural Optimization of Neural Networks

Kaitlin Maile, Dennis George Wilson, Patrick Forré

Planning with Large Language Models for Code Generation

Shun Zhang, Zhenfang Chen, Yikang Shen, Mingyu Ding, Joshua B. Tenenbaum

Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees

Swarnadeep Saha, Shiyue Zhang, Peter Hase, Mohit Bansal

Symmetries, Flat Minima, and the Conserved Quantities of Gradient Flow

Bo Zhao, Iordan Ganev, Robin Walters, Rose Yu, Nima Dehmamy

Hyperparameter Optimization through Neural Network Partitioning

Bruno Kacper Mlodozeniec, Matthias Reisser, Christos Louizos

Few-shot Backdoor Attacks via Neural Tangent Kernels

Jonathan Hayase, Sewoong Oh

Noise-Robust De-Duplication at Scale

Emily Silcock, Luca D'Amico-Wong, Jinglin Yang, Melissa Dell

Logical Message Passing Networks with One-hop Inference on Atomic Formulas

Zihao Wang, Yangqiu Song, Ginny Wong, Simon See

Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation

Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess

Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme Ruiz, Basil Mustafa

Variational Information Pursuit for Interpretable Predictions

Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin David Haeffele, Donald Geman, René Vidal

Revisiting Robustness in Graph Machine Learning

Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann

Understanding Train-Validation Split in Meta-Learning with Neural Networks

Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu

CrAM: A Compression-Aware Minimizer

Alexandra Peste, Adrian Vladu, Eldar Kurtic, Christoph H Lampert, Dan Alistarh

Transfer Learning with Deep Tabular Models

Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss

FIT: A Metric for Model Sensitivity

Ben Zandonati, Adrian Alan Pol, Maurizio Pierini, Olya Sirkin, Tal Kopetz

Investigating Multi-task Pretraining and Generalization in Reinforcement Learning

Adrien Ali Taiga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, Marc G Bellemare

Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images

Yufei CUI, Ziquan Liu, Xiangyu Liu, Xue Liu, Cong Wang

Long Range Language Modeling via Gated State Spaces

Harsh Mehta, Ankit Gupta, Ashok Cutkosky, Behnam Neyshabur

Phenaki: Variable Length Video Generation from Open Domain Textual Descriptions

Ruben Villegas, Mohammad Babaeizadeh, Pieter-jan Kindermans, Hernan Moraldo, Han Zhang

Differentially Private Adaptive Optimization with Delayed Preconditioners

Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan

SemPPL: Predicting Pseudo-Labels for Better Contrastive Representations

Matko Bošnjak, Pierre Harvey Richemond, Nenad Tomasev, Florian Strub, Jacob C Walker

Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks

Zhen Lin, Shubhendu Trivedi, Jimeng Sun

Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning

Sheng Zhang, Hao Cheng, Jianfeng Gao, Hoifung Poon

Effective passive membership inference attacks in federated learning against overparameterized models

Jiacheng Li, Ninghui Li, Bruno Ribeiro

DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics

Sizhe Li, Zhiao Huang, Tao Chen, Tao Du, Hao Su

Composing Task Knowledge With Modular Successor Feature Approximators

Wilka Torrico Carvalho, Angelos Filos, Richard Lewis, Honglak Lee, Satinder Singh

Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs

Haotian Fu, Jiayu Yao, Omer Gottesman, Finale Doshi-velez, George Konidaris

Distributed Extra-gradient with Optimal Complexity and Communication Guarantees

Ali Ramezani-Kebrya, Kimon Antonakopoulos, Igor Krawczuk, Justin Deschenaux, Volkan Cevher

Accelerated Single-Call Methods for Constrained Min-Max Optimization

Yang Cai, Weiqiang Zheng

ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond

Xiaojun Guo, Yifei Wang, Tianqi Du, Yisen Wang

Don’t forget the nullspace! Nullspace occupancy as a mechanism for out of distribution failure

Daksh Idnani, Vivek Madan, Naman Goyal, David J. Schwab, Shanmukha Ramakrishna Vedantam

Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning

Fahad Sarfraz, Elahe Arani, Bahram Zonooz

Verifying the Union of Manifolds Hypothesis for Image Data

Bradley CA Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C Cresswell, Gabriel Loaiza-Ganem

Planning with Sequence Models through Iterative Energy Minimization

Hongyi Chen, Yilun Du, Yiye Chen, Joshua B. Tenenbaum, Patricio A. Vela

Pseudoinverse-Guided Diffusion Models for Inverse Problems

Jiaming Song, Arash Vahdat, Morteza Mardani, Jan Kautz

Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation

Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou

Evaluating Representations with Readout Model Switching

Yazhe Li, Jörg Bornschein, Marcus Hutter

How gradient estimator variance and bias impact learning in neural networks

Arna Ghosh, Yuhan Helena Liu, Guillaume Lajoie, Konrad Kording, Blake Aaron Richards