ICML 2024

2,610 papers

WARM: On the Benefits of Weight Averaged Reward Models

Alexandre Rame, Nino Vieillard, Leonard Hussenot, Robert Dadashi-Tazehozi, Geoffrey Cideron

STEER: Assessing the Economic Rationality of Large Language Models

Narun Krishnamurthi Raman, Taylor Lundy, Samuel Joseph Amouyal, Yoav Levine, Kevin Leyton-brown

Understanding Inter-Concept Relationships in Concept-Based Models

Naveen Janaki Raman, Mateo Espinosa Zarlenga, Mateja Jamnik

In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization

Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Eddie Bergman

Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation

Ossi Räisä, Joonas Jälkö, Antti Honkela

Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints

Mohammad Reza Rahmani, Mohammad Hossein Yassaee, Mohammad Ali Maddah-Ali, Mohammad Reza Aref

Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference

Md Musfiqur Rahman, Murat Kocaoglu

A New Robust Partial p-Wasserstein-Based Metric for Comparing Distributions

Sharath Raghvendra, Pouyan Shirzadian, Kaiyi Zhang

Decomposable Submodular Maximization in Federated Setting

Akbar Rafiey

Multiply-Robust Causal Change Attribution

Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, David Heckerman

Augmenting Decision with Hypothesis in Reinforcement Learning

Nguyen Minh Quang, Hady W. Lauw

Learning Constraints from Offline Demonstrations via Superior Distribution Correction Estimation

Guorui Quan, Zhiqiang Xu, Guiliang Liu

Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations

Helen Qu, Sang Michael Xie

MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space

Yanru Qu, Keyue Qiu, Yuxuan Song, Jingjing Gong, Jiawei Han

Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimization

Ruizhong Qiu, Hanghang Tong

Compute Better Spent: Replacing Dense Layers with Structured Matrices

Shikai Qiu, Andres Potapczynski, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson

Complexity Matters: Feature Learning in the Presence of Spurious Correlations

Guanwen Qiu, Da Kuang, Surbhi Goel

Transferring Knowledge From Large Foundation Models to Small Downstream Models

Shikai Qiu, Boran Han, Danielle C. Maddix, SHUAI ZHANG, Bernie Wang

To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO

Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang

Learning High-Order Relationships of Brain Regions

Weikang Qiu, Huangrui Chu, Selena Wang, Haolan Zuo, Xiaoxiao Li

Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks

Zirou Qiu, Abhijin Adiga, Madhav Marathe, S. S. Ravi, Daniel Rosenkrantz

Feasible Reachable Policy Iteration

Shentao Qin, Yujie Yang, Yao Mu, Jie Li, Wenjun Zou

Various Lengths, Constant Speed: Efficient Language Modeling with Lightning Attention

Zhen Qin, Weigao Sun, Dong Li, Xuyang Shen, Weixuan Sun

Accurate LoRA-Finetuning Quantization of LLMs via Information Retention

Haotong Qin, Xudong Ma, Xingyu Zheng, Xiaoyang Li, Yang Zhang

Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes

Zhen Qin, Daoyuan Chen, Bingchen Qian, Bolin Ding, Yaliang Li

ULAREF: A Unified Label Refinement Framework for Learning with Inaccurate Supervision

Congyu Qiao, Ning Xu, Yihao Hu, Xin Geng

Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints

Dan Qiao, Yu-Xiang Wang

Ensemble Pruning for Out-of-distribution Generalization

Fengchun Qiao, Xi Peng

Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation

Yu-Yang Qian, Peng Zhao, Yu-Jie Zhang, Masashi Sugiyama, Zhi-Hua Zhou

ByMI: Byzantine Machine Identification with False Discovery Rate Control

Chengde Qian, Mengyuan Wang, Haojie Ren, Changliang Zou

Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning

Long Qian, Juncheng Li, Yu Wu, Yaobo Ye, Hao Fei

Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration

Shi-ang Qi, Yakun Yu, Russell Greiner

Hierarchical Novelty Detection via Fine-Grained Evidence Allocation

Spandan Pyakurel, Qi Yu

On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions

Denys Pushkin, Raphaël Berthier, Emmanuel Abbe

Learning to Remove Cuts in Integer Linear Programming

Pol Puigdemont, Stratis Skoulakis, Grigorios Chrysos, Volkan Cevher

Amortizing Pragmatic Program Synthesis with Rankings

Yewen Pu, Saujas Vaduguru, Priyan Vaithilingam, Elena Glassman, Daniel Fried

Unsupervised Domain Adaptation for Anatomical Structure Detection in Ultrasound Images

Bin Pu, Xingguo Lv, Jiewen Yang, He Guannan, Xingbo Dong

Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation

Yuanhao Pu, Xiaolong Chen, Xu Huang, Jin Chen, Defu Lian

Learning a Diffusion Model Policy from Rewards via Q-Score Matching

Michael Psenka, Alejandro Escontrela, Pieter Abbeel, Yi Ma

Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling

Danil Provodin, Maurits Clemens Kaptein, Mykola Pechenizkiy

Autoencoding Conditional Neural Processes for Representation Learning

Victor Prokhorov, Ivan Titov, Siddharth N

Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)

Drew Prinster, Samuel Don Stanton, Anqi Liu, Suchi Saria

The Entropy Enigma: Success and Failure of Entropy Minimization

Ori Press, Ravid Shwartz-Ziv, Yann LeCun, Matthias Bethge

MusicFlow: Cascaded Flow Matching for Text Guided Music Generation

K R Prajwal, Bowen Shi, Matthew Le, Apoorv Vyas, Andros Tjandra

Learning Multiple Secrets in Mastermind

Milind Prabhu, David Woodruff

Stochastic Optimization with Arbitrary Recurrent Data Sampling

William Powell, Hanbaek Lyu

Robust Data-driven Prescriptiveness Optimization

Mehran Poursoltani, Erick Delage, Angelos Georghiou

Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise

Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar

Mechanistic Design and Scaling of Hybrid Architectures

Michael Poli, Armin W Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth

Adaptive Conformal Inference by Betting

Aleksandr Podkopaev, Dong Xu, Kuang-chih Lee

Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach

Anton Plaksin, Vitaly Kalev

diff History for Neural Language Agents

Ulyana Piterbarg, Lerrel Pinto, Rob Fergus

Contrasting Multiple Representations with the Multi-Marginal Matching Gap

Zoe Piran, Michal Klein, James Thornton, Marco Cuturi

Extracting Training Data From Document-Based VQA Models

Francesco Pinto, Nathalie Rauschmayr, Florian Tramer, Philip Torr, Federico Tombari

Structure-based drug design by denoising voxel grids

Pedro O. Pinheiro, Arian Rokkum Jamasb, Omar Mahmood, Vishnu Sresht, Saeed Saremi

Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration via Shift Reduction Lemmas

Clément Pierquin, Aurélien Bellet, Marc Tommasi, Matthieu Boussard

Detecting Influence Structures in Multi-Agent Reinforcement Learning

Fabian Raoul Pieroth, Katherine Fitch, Lenz Belzner

Federated Continual Learning via Prompt-based Dual Knowledge Transfer

Hongming Piao, Yichen WU, Dapeng Wu, Ying Wei

Particle Denoising Diffusion Sampler

Angus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis

Controllable Prompt Tuning For Balancing Group Distributional Robustness

Hoang Phan, Andrew Gordon Wilson, Qi Lei

When is Transfer Learning Possible?

My Phan, Kianté Brantley, Stephanie Milani, Soroush Mehri, Gokul Swamy

Cross-view Masked Diffusion Transformers for Person Image Synthesis

Trung X. Pham, Kang Zhang, Chang D. Yoo

Neural NeRF Compression

Tuan Pham, Stephan Mandt

Transport of Algebraic Structure to Latent Embeddings

Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi

Discovering Environments with XRM

Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent

Prompting a Pretrained Transformer Can Be a Universal Approximator

Aleksandar Petrov, Philip Torr, Adel Bibi

Bayesian Regret Minimization in Offline Bandits

Marek Petrik, Guy Tennenholtz, Mohammad Ghavamzadeh

Mechanistic Neural Networks for Scientific Machine Learning

Adeel Pervez, Francesco Locatello, Stratis Gavves

Interpreting and Improving Diffusion Models from an Optimization Perspective

Frank Permenter, Chenyang Yuan

The Relative Value of Prediction in Algorithmic Decision Making

Juan Carlos Perdomo

Solving Hierarchical Information-Sharing Dec-POMDPs: An Extensive-Form Game Approach

Johan Peralez, Aurélien Delage, Olivier Buffet, Jilles Steeve Dibangoye

CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources

Sikha Pentyala, Mayana Pereira, Martine De Cock

A Subquadratic Time Algorithm for Robust Sparse Mean Estimation

Ankit Pensia

Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance

Xinyu Peng, Ziyang Zheng, Wenrui Dai, Nuoqian Xiao, Chenglin Li

FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler

Hongyi Peng, Han Yu, Xiaoli Tang, Xiaoxiao Li

Block Acceleration Without Momentum: On Optimal Stepsizes of Block Gradient Descent for Least-Squares

Liangzu Peng, Wotao Yin

UPOCR: Towards Unified Pixel-Level OCR Interface

Dezhi Peng, Zhenhua Yang, Jiaxin Zhang, Chongyu Liu, Yongxin Shi

Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input

Andi Peng, Yuying Sun, Tianmin Shu, David Abel

eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data

Bo Peng, Xinyi Ling, Ziru Chen, Huan Sun, Xia Ning

UPAM: Unified Prompt Attack in Text-to-Image Generation Models Against Both Textual Filters and Visual Checkers

Duo Peng, Qiuhong Ke, Jun Liu

Knowledge Distillation with Auxiliary Variable

Bo Peng, Zhen Fang, Guangquan Zhang, Jie Lu

Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks

Amit Peleg, Matthias Hein

BetterV: Controlled Verilog Generation with Discriminative Guidance

Zehua Pei, Huiling Zhen, Mingxuan Yuan, Yu Huang, Bei Yu

Data-free Neural Representation Compression with Riemannian Neural Dynamics

Zhengqi Pei, Anran Zhang, Shuhui Wang, Xiangyang Ji, Qingming Huang

Modeling Language Tokens as Functionals of Semantic Fields

Zhengqi Pei, Anran Zhang, Shuhui Wang, Qingming Huang

Exploiting Code Symmetries for Learning Program Semantics

Kexin Pei, Weichen Li, Qirui Jin, Shuyang Liu, Scott Geng

Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing

Hongbin Pei, Yu Li, Huiqi Deng, Jingxin Hai, Pinghui Wang

Graph Automorphism Group Equivariant Neural Networks

Edward Pearce-Crump, William Knottenbelt

In-Context Unlearning: Language Models as Few-Shot Unlearners

Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju

Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces

Brahma S Pavse, Matthew Zurek, Yudong Chen, Qiaomin Xie, Josiah P. Hanna

LPGD: A General Framework for Backpropagation through Embedded Optimization Layers

Anselm Paulus, Georg Martius, Vı́t Musil

Estimating Canopy Height at Scale

Jan Pauls, Max Zimmer, Una M. Kelly, Martin Schwartz, Sassan Saatchi

PerceptAnon: Exploring the Human Perception of Image Anonymization Beyond Pseudonymization for GDPR

Kartik Patwari, Chen-Nee Chuah, Lingjuan Lyu, Vivek Sharma

Optimal Ridge Regularization for Out-of-Distribution Prediction

Pratik Patil, Jin-Hong Du, Ryan Tibshirani

Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles

Bhrij Patel, Wesley A Suttle, Alec Koppel, Vaneet Aggarwal, Brian M. Sadler

Variational Inference with Coverage Guarantees in Simulation-Based Inference

Yash Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari

State-Free Inference of State-Space Models: The *Transfer Function* Approach

Rom Parnichkun, Stefano Massaroli, Alessandro Moro, Jimmy T.H. Smith, Ramin Hasani

BOtied: Multi-objective Bayesian optimization with tied multivariate ranks

Ji Won Park, Natasa Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho

Can Mamba Learn How To Learn? A Comparative Study on In-Context Learning Tasks

Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho

Position: Automatic Environment Shaping is the Next Frontier in RL

Younghyo Park, Gabriel B. Margolis, Pulkit Agrawal