ICML 2023

1,828 papers

Invariance in Policy Optimisation and Partial Identifiability in Reward Learning

Joar Max Viktor Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, Adam Gleave

Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems

Chawin Sitawarin, Florian Tramer, Nicholas Carlini

Differentiable Simulations for Enhanced Sampling of Rare Events

Martin Sipka, Johannes C. B. Dietschreit, Lukáš Grajciar, Rafael Gomez-Bombarelli

When do Minimax-fair Learning and Empirical Risk Minimization Coincide?

Harvineet Singh, Matthäus Kleindessner, Volkan Cevher, Rumi Chunara, Chris Russell

The Hessian perspective into the Nature of Convolutional Neural Networks

Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf

Text-To-4D Dynamic Scene Generation

Uriel Singer, Shelly Sheynin, Adam Polyak, Oron Ashual, Iurii Makarov

Hindsight Learning for MDPs with Exogenous Inputs

Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo De Oliveira Barbalho

On the Stepwise Nature of Self-Supervised Learning

James B Simon, Maksis Knutins, Liu Ziyin, Daniel Geisz, Abraham J Fetterman

Statistical Learning under Heterogeneous Distribution Shift

Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy

Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk

David Simchi-Levi, Chonghuan Wang

Quantitative Universal Approximation Bounds for Deep Belief Networks

Julian Sieber, Johann Gehringer

Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data

Ali Siahkoohi, Rudy Morel, Maarten V. de Hoop, Erwan Allys, Gregory Sainton

Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows

Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov

CLIPood: Generalizing CLIP to Out-of-Distributions

Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang

Repository-Level Prompt Generation for Large Language Models of Code

Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

Causal Bounds in Quasi-Markovian Graphs

Madhumitha Shridharan, Garud Iyengar

Probabilistic Attention-to-Influence Neural Models for Event Sequences

Xiao Shou, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Oktie Hassanzadeh

Synthetic data for model selection

Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky, Matan Fintz, Gérard Medioni

Exphormer: Sparse Transformers for Graphs

Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Ali Kemal Sinop

Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting

Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley

Context Consistency Regularization for Label Sparsity in Time Series

Yooju Shin, Susik Yoon, Hwanjun Song, Dongmin Park, Byunghyun Kim

One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill

Sangwoo Shin, Daehee Lee, Minjong Yoo, Woo Kyung Kim, Honguk Woo

Improved Learning-Augmented Algorithms for the Multi-Option Ski Rental Problem via Best-Possible Competitive Analysis

Yongho Shin, Changyeol Lee, Gukryeol Lee, Hyung-Chan An

MetricGAN-OKD: Multi-Metric Optimization of MetricGAN via Online Knowledge Distillation for Speech Enhancement

WooSeok Shin, Byung Hoon Lee, Jin Sob Kim, Hyun Joon Park, Sung Won Han

A Closer Look at the Intervention Procedure of Concept Bottleneck Models

Sungbin Shin, Yohan Jo, Sungsoo Ahn, Namhoon Lee

SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning

Dongseok Shim, Seungjae Lee, H. Jin Kim

Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space

Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh

Long Horizon Temperature Scaling

Andy Shih, Dorsa Sadigh, Stefano Ermon

Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective

Liangliang Shi, Gu Zhang, Haoyu Zhen, Jintao Fan, Junchi Yan

On the Complexity of Bayesian Generalization

Yu-Zhe Shi, Manjie Xu, John E. Hopcroft, Kun He, Joshua B. Tenenbaum

Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources

Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang

Statistical Inference on Multi-armed Bandits with Delayed Feedback

Lei Shi, Jingshen Wang, Tianhao Wu

Sequence Modeling with Multiresolution Convolutional Memory

Jiaxin Shi, Ke Alexander Wang, Emily Fox

UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers

Dachuan Shi, Chaofan Tao, Ying Jin, Zhendong Yang, Chun Yuan

Improving the Model Consistency of Decentralized Federated Learning

Yifan Shi, Li Shen, Kang Wei, Yan Sun, Bo Yuan

A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints

Ming Shi, Yingbin Liang, Ness Shroff

Everyone’s Preference Changes Differently: A Weighted Multi-Interest Model For Retrieval

Hui Shi, Yupeng Gu, Yitong Zhou, Bo Zhao, Sicun Gao

Large Language Models Can Be Easily Distracted by Irrelevant Context

Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan

Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods

Aleksandr Shevchenko, Kevin Kögler, Hamed Hassani, Marco Mondelli

Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation

Uri Sherman, Tomer Koren, Yishay Mansour

FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin

TGRL: An Algorithm for Teacher Guided Reinforcement Learning

Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal

Auxiliary Modality Learning with Generalized Curriculum Distillation

Yu Shen, Xijun Wang, peng gao, Ming Lin

Cross-Modal Fine-Tuning: Align then Refine

Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak

Non-autoregressive Conditional Diffusion Models for Time Series Prediction

Lifeng Shen, James Kwok

On Penalty-based Bilevel Gradient Descent Method

Han Shen, Tianyi Chen

On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation

Maohao Shen, Yuheng Bu, Gregory W. Wornell

Towards Understanding and Improving GFlowNet Training

Max W Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas, Kyunghyun Cho

Cold Analysis of Rao-Blackwellized Straight-Through Gumbel-Softmax Gradient Estimator

Alexander Shekhovtsov

Sequential Changepoint Detection via Backward Confidence Sequences

Shubhanshu Shekhar, Aaditya Ramdas

On Kinetic Optimal Probability Paths for Generative Models

Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matthew Le, Yaron Lipman

Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates

Louis Sharrock, Christopher Nemeth

Toward Efficient Gradient-Based Value Estimation

Arsalan Sharifnassab, Richard S. Sutton

Regularization-free Diffeomorphic Temporal Alignment Nets

Ron Shapira Weber, Oren Freifeld

Complementary Attention for Multi-Agent Reinforcement Learning

Jianzhun Shao, Hongchang Zhang, Yun Qu, Chang Liu, Shuncheng He

Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models

Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan

Auxiliary Learning as an Asymmetric Bargaining Game

Aviv Shamsian, Aviv Navon, Neta Glazer, Kenji Kawaguchi, Gal Chechik

ModelDiff: A Framework for Comparing Learning Algorithms

Harshay Shah, Sung Min Park, Andrew Ilyas, Aleksander Madry

One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding

Daniel Severo, James Townsend, Ashish J Khisti, Alireza Makhzani

Multi-View Masked World Models for Visual Robotic Manipulation

Younggyo Seo, Junsu Kim, Stephen James, Kimin Lee, Jinwoo Shin

Explainability as statistical inference

Hugo Henri Joseph Senetaire, Damien Garreau, Jes Frellsen, Pierre-Alexandre Mattei

Incentivizing Exploration with Linear Contexts and Combinatorial Actions

Mark Sellke

Bayesian online change point detection with Hilbert space approximate Student-t process

Jeremy Sellier, Petros Dellaportas

Neural Markov Jump Processes

Patrick Seifner, Ramses J Sanchez

Variational Autoencoding Neural Operators

Jacob H Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris

Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language

Philipp Seidl, Andreu Vall, Sepp Hochreiter, Günter Klambauer

Double-Weighting for Covariate Shift Adaptation

José I. Segovia-Martín, Santiago Mazuelas, Anqi Liu

A Fast Optimistic Method for Monotone Variational Inequalities

Michael Sedlmayer, Dang-Khoa Nguyen, Radu Ioan Bot

Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning

Antonio Sclocchi, Mario Geiger, Matthieu Wyart

Bigger, Better, Faster: Human-level Atari with human-level efficiency

Max Schwarzer, Johan Samir Obando Ceron, Aaron Courville, Marc G Bellemare, Rishabh Agarwal

Modality-Agnostic Variational Compression of Implicit Neural Representations

Jonathan Richard Schwarz, Jihoon Tack, Yee Whye Teh, Jaeho Lee, Jinwoo Shin

The Acquisition of Physical Knowledge in Generative Neural Networks

Luca M. Schulze Buschoff, Eric Schulz, Marcel Binz

Deterministic equivalent and error universality of deep random features learning

Dominik Schröder, Hugo Cui, Daniil Dmitriev, Bruno Loureiro

Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks

Dominik Schnaus, Jongseok Lee, Daniel Cremers, Rudolph Triebel

Identifiability and Generalizability in Constrained Inverse Reinforcement Learning

Andreas Schlaginhaufen, Maryam Kamgarpour

Gibbsian Polar Slice Sampling

Philip Schär, Michael Habeck, Daniel Rudolf

Off-Policy Average Reward Actor-Critic with Deterministic Policy Search

Naman Saxena, Subhojyoti Khastagir, Shishir Kolathaya, Shalabh Bhatnagar

Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction

Andrey Savchenko

StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

Axel Sauer, Tero Karras, Samuli Laine, Andreas Geiger, Timo Aila

Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule

Naoki Sato, Hideaki Iiduka

Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure

Ryoma Sato

Posterior Sampling for Deep Reinforcement Learning

Remo Sasso, Michelangelo Conserva, Paulo Rauber

Random Teachers are Good Teachers

Felix Sarnthein, Gregor Bachmann, Sotiris Anagnostidis, Thomas Hofmann

Streaming Active Learning with Deep Neural Networks

Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash

Whose Opinions Do Language Models Reflect?

Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang

Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models

Parth Vipul Sangani, Arjun Shashank Kashettiwar, Pritish Chakraborty, Bhuvan Reddy Gangula, Durga S

TAN Without a Burn: Scaling Laws of DP-SGD

Tom Sander, Pierre Stock, Alexandre Sablayrolles

Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective

Michael Eli Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel

Raising the Cost of Malicious AI-Powered Image Editing

Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, Aleksander Madry

Optimizing Hyperparameters with Conformal Quantile Regression

David Salinas, Jacek Golebiowski, Aaron Klein, Matthias Seeger, Cédric Archambeau

Distributed Linear Bandits under Communication Constraints

Sudeep Salgia, Qing Zhao

Provably and Practically Efficient Neural Contextual Bandits

Sudeep Salgia

PAC-Bayesian Offline Contextual Bandits With Guarantees

Otmane Sakhi, Pierre Alquier, Nicolas Chopin

Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster $\text{L}$-/$\text{L}^\natural$-Convex Function Minimization

Shinsaku Sakaue, Taihei Oki

Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling

Yuta Saito, Qingyang Ren, Thorsten Joachims

Multi-class Graph Clustering via Approximated Effective p-Resistance

Shota Saito, Mark Herbster

Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement

Eden Saig, Nir Rosenfeld

End-to-end Differentiable Clustering with Associative Memories

Bishwajit Saha, Dmitry Krotov, Mohammed J Zaki, Parikshit Ram

High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance

Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel

Global Selection of Contrastive Batches via Optimization on Sample Permutations

Vin Sachidananda, Ziyi Yang, Chenguang Zhu