ICML

Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning

Xiangzhe Kong, Wenbing Huang, Yang Liu

Comments Accepted to ICML 2024

O$n$ Learning Deep O($n$)-Equivariant Hyperspheres

Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck, Andreas Robinson, Cuong Le

Comments Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024

Rich-Observation Reinforcement Learning with Continuous Latent Dynamics

Yuda Song, Lili Wu, Dylan J. Foster, Akshay Krishnamurthy

Comments 63 pages, 4 figures, published at ICML 2024

Online Linear Regression in Dynamic Environments via Discounting

Andrew Jacobsen, Ashok Cutkosky

Comments ICML 2024, 38 pages

Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior

Shuyu Cheng, Yibo Miao, Yinpeng Dong, Xiao Yang, Xiao-Shan Gao, Jun Zhu

Comments ICML 2024

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

Danil Provodin, Maurits Kaptein, Mykola Pechenizkiy

Comments To appear at ICML'24

Kernel Semi-Implicit Variational Inference

Ziheng Cheng, Longlin Yu, Tianyu Xie, Shiyue Zhang, Cheng Zhang

Comments ICML 2024 camera ready

Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space

Minji Lee, Luiz Felipe Vecchietti, Hyunkyu Jung, Hyun Joo Ro, Meeyoung Cha, Ho Min Kim

Comments ICML 2024

Language Generation with Strictly Proper Scoring Rules

Chenze Shao, Fandong Meng, Yijin Liu, Jie Zhou

Comments ICML 2024

Counterfactual Metarules for Local and Global Recourse

Tom Bewley, Salim I. Amoukou, Saumitra Mishra, Daniele Magazzeni, Manuela Veloso

Comments Accepted at ICML 2024

Learning to Continually Learn with the Bayesian Principle

Soochan Lee, Hyeonseong Jeon, Jaehyeon Son, Gunhee Kim

Comments ICML 2024

Provable Contrastive Continual Learning

Yichen Wen, Zhiquan Tan, Kaipeng Zheng, Chuanlong Xie, Weiran Huang

Comments Accepted by ICML 2024

When and How Does In-Distribution Label Help Out-of-Distribution Detection?

Xuefeng Du, Yiyou Sun, Yixuan Li

Comments ICML 2024

A Margin-based Multiclass Generalization Bound via Geometric Complexity

Michael Munn, Benoit Dherin, Javier Gonzalvo

Comments Accepted as an ICML 2023 workshop paper (Topology, Algebra and Geometry in Machine Learning)

Journal ref Proceedings of 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML), PMLR 221:189-205, 2023

Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization

Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon

Comments To appear in ICML 2024

Position: Foundation Agents as the Paradigm Shift for Decision Making

Xiaoqian Liu, Xingzhou Lou, Jianbin Jiao, Junge Zhang

Comments 17 pages, camera-ready version of ICML 2024

Test-Time Model Adaptation with Only Forward Passes

Shuaicheng Niu, Chunyan Miao, Guohao Chen, Pengcheng Wu, Peilin Zhao

Comments 18 pages, 4 figures, 17 tables, accepted by International Conference on Machine Learning

Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion

Hila Manor, Tomer Michaeli

Comments Accepted for ICML 2024; Examples and code available in https://hilamanor.github.io/AudioEditing/

Nesting Particle Filters for Experimental Design in Dynamical Systems

Sahel Iqbal, Adrien Corenflos, Simo Särkkä, Hany Abdulsamad

Comments Accepted to ICML 2024

Position: Optimization in SciML Should Employ the Function Space Geometry

Johannes Müller, Marius Zeinhofer

Comments 9 pages main body, 2 figures, 1 table

Journal ref Proceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024

Generalized Preference Optimization: A Unified Approach to Offline Alignment

Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot

Comments Accepted at ICML 2023 main conference

Principled Preferential Bayesian Optimization

Wenjie Xu, Wenbin Wang, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones

Comments Accepted to ICML 2024

A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?

Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss

Comments ICML 2024. Code: https://github.com/wiseodd/lapeft-bayesopt

Generalized Sobolev Transport for Probability Measures on a Graph

Tam Le, Truyen Nguyen, Kenji Fukumizu

Comments To appear at ICML'2024

More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms

Hossein Zakerinia, Amin Behjati, Christoph H. Lampert

Comments International Conference on Machine Learning (ICML), 2024

Diffusive Gibbs Sampling

Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber

Comments Accepted for publication at ICML 2024. Code available: https://github.com/Wenlin-Chen/DiGS

Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks

Zhuomin Chen, Jiaxing Zhang, Jingchao Ni, Xiaoting Li, Yuchen Bian, Md Mezbahul Islam, Ananda Mohan Mondal, Hua Wei, Dongsheng Luo

Comments Accepted to International Conference on Machine Learning (ICML 2024)

Building Guardrails for Large Language Models

Yi Dong, Ronghui Mu, Gaojie Jin, Yi Qi, Jinwei Hu, Xingyu Zhao, Jie Meng, Wenjie Ruan, Xiaowei Huang

Comments Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024

Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning

Zhuo Huang, Chang Liu, Yinpeng Dong, Hang Su, Shibao Zheng, Tongliang Liu

Comments ICML 2024

InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining

Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, Bryan Catanzaro

Comments ICML 2024