ICML

End-to-End Optimized Pipeline for Prediction of Protein Folding Kinetics

Vijay Arvind. R, Haribharathi Sivakumar, Brindha. R

Comments Accepted for presentation at the 22nd International Conference on Machine Learning and Applications

Diffusion Generative Inverse Design

Marin Vlastelica, Tatiana López-Guevara, Kelsey Allen, Peter Battaglia, Arnaud Doucet, Kimberley Stachenfeld

Comments ICML workshop on Structured Probabilistic Inference & Generative Modeling

When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee

Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang

Comments 25 pages

Journal ref Proceedings of the 39th International Conference on Machine Learning, 2022

Guiding Pretraining in Reinforcement Learning with Large Language Models

Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, Jacob Andreas

Comments ICML 2023

Reinforcement Learning for Supply Chain Attacks Against Frequency and Voltage Control

Amr S. Mohamed, Sumin Lee, Deepa Kundur

Comments 7 pages, conference, IEEE International Conference on Machine Learning and Applications (ICMLA) 2023

Distributionally Robust Batch Contextual Bandits

Nian Si, Fan Zhang, Zhengyuan Zhou, Jose Blanchet

Comments The short version has been accepted in ICML 2020

Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks

Kacem Khaled, Mouna Dhaouadi, Felipe Gohring de Magalhães, Gabriela Nicolescu

Comments Accepted for publication at 2023 International Conference on Machine Learning and Applications (ICMLA). Proceedings of ICMLA, Florida, USA \c{opyright}2023 IEEE

Faster, Lighter, More Accurate: A Deep Learning Ensemble for Content Moderation

Mohammad Hosseini, Mahmudul Hasan

Comments 6 pages, 22nd IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'23), December 15-17, 2023, Jacksonville Riverfront, Florida, USA. arXiv admin note: substantial text overlap with arXiv:2103.10350

Towards Trustworthy Explanation: On Causal Rationalization

Wenbo Zhang, Tong Wu, Yunlong Wang, Yong Cai, Hengrui Cai

Comments In Proceedings of the 40th International Conference on Machine Learning (ICML) GitHub Repository: https://github.com/onepounchman/Causal-Retionalization

Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation

Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka

Comments Accepted by ICML 2023

AudioLDM: Text-to-Audio Generation with Latent Diffusion Models

Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, Mark D. Plumbley

Comments Accepted by ICML 2023. Demo and implementation at https://audioldm.github.io. Evaluation toolbox at https://github.com/haoheliu/audioldm_eval

Identifying Interpretable Subspaces in Image Representations

Neha Kalibhat, Shweta Bhardwaj, Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi

Comments Published at ICML 2023 Code: https://github.com/NehaKalibhat/falcon-explain

Internet Explorer: Targeted Representation Learning on the Open Web

Alexander C. Li, Ellis Brown, Alexei A. Efros, Deepak Pathak

Comments In ICML 2023. Website at https://internet-explorer-ssl.github.io/

Learning Representations that Support Extrapolation

Taylor W. Webb, Zachary Dulberg, Steven M. Frankland, Alexander A. Petrov, Randall C. O'Reilly, Jonathan D. Cohen

Comments ICML 2020

MyoDex: A Generalizable Prior for Dexterous Manipulation

Vittorio Caggiano, Sudeep Dasari, Vikash Kumar

Comments Accepted to the 40th International Conference on Machine Learning (2023)

Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization

Shadi Ghiasi, Guido Pazzi, Concettina Del Grosso, Giovanni De Magistris, Giacomo Veneri

Comments Accepted after peer-review at the 1st workshop on Synergy of Scientific and Machine Learning Modeling, SynS & ML ICML, Honolulu, Hawaii, USA. July, 2023. Copyright 2023 by the author(s)

Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation

Mohamad Qadri, Michael Kaess

Comments 6 pages, 4 figures. Published at the Differentiable Almost Everything Workshop of the 40th International Conference on Machine Learning

Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning

Gaurav Bagwe, Xiaoyong Yuan, Miao Pan, Lan Zhang

Comments Accepted by FL-ICML 2023

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Hugo Cui, Florent Krzakala, Lenka Zdeborová

Journal ref Proceedings of the 40th International Conference on Machine Learning, PMLR 202:6468-6521, 2023

Efficient RL via Disentangled Environment and Agent Representations

Kevin Gmelin, Shikhar Bahl, Russell Mendonca, Deepak Pathak

Comments ICML 2023. Website at https://sear-rl.github.io/

Sparse Function-space Representation of Neural Networks

Aidan Scannell, Riccardo Mereu, Paul Chang, Ella Tamir, Joni Pajarinen, Arno Solin

Comments Accepted to ICML 2023 Workshop on Duality for Modern Machine Learning, Honolulu, Hawaii, USA. 4 pages, 2 figures, 1 table

Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction

Minghao Guo, Veronika Thost, Samuel W Song, Adithya Balachandran, Payel Das, Jie Chen, Wojciech Matusik

Comments 22 pages, 10 figures; ICML 2023

FedFwd: Federated Learning without Backpropagation

Seonghwan Park, Dahun Shin, Jinseok Chung, Namhoon Lee

Comments ICML 2023 Workshop (Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities)

Doubly Optimal No-Regret Learning in Monotone Games

Yang Cai, Weiqiang Zheng

Comments Published at ICML 2023. V2 incorporates reviewers' feedback

ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data

Ulyana Tkachenko, Aditya Thyagarajan, Jonas Mueller

Comments ICML Workshop on Data-centric Machine Learning Research

Why do universal adversarial attacks work on large language models?: Geometry might be the answer

Varshini Subhash, Anna Bialas, Weiwei Pan, Finale Doshi-Velez

Comments 2nd AdvML Frontiers Workshop at 40th International Conference on Machine Learning, Honolulu, Hawaii, USA, 2023

Fairness in Preference-based Reinforcement Learning

Umer Siddique, Abhinav Sinha, Yongcan Cao

Comments Accepted to The Many Facets of Preference Learning Workshop at the International Conference on Machine Learning (ICML)

LEVER: Learning to Verify Language-to-Code Generation with Execution

Ansong Ni, Srini Iyer, Dragomir Radev, Ves Stoyanov, Wen-tau Yih, Sida I. Wang, Xi Victoria Lin

Comments ICML'23; code available at https://github.com/niansong1996/lever

On the Equivalence between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint

Zenan Ling, Zhenyu Liao, Robert C. Qiu

Comments Accepted by Workshop on High-dimensional Learning Dynamics, ICML 2023, Honolulu, Hawaii

Flexible Phase Dynamics for Bio-Plausible Contrastive Learning

Ezekiel Williams, Colin Bredenberg, Guillaume Lajoie

Comments 23 pages, 4 figures. Paper accepted to ICML and update includes changes made based on reviewer feedback

Journal ref PMLR 202:37042-37065, 2023