ICML

Evaluating Deepfake Detectors in the Wild

Viacheslav Pirogov, Maksim Artemev

Comments Accepted to the ICML 2025 Workshop 'DataWorld: Unifying Data Curation Frameworks Across Domains'

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts

Mateo Espinosa Zarlenga, Gabriele Dominici, Pietro Barbiero, Zohreh Shams, Mateja Jamnik

机构 * University of Cambridge（剑桥大学） ； Leap Laboratories Inc.（Leap实验室公司） ； IBM Research（IBM研究院） 纠错

Comments Presented at the Forty-Second International Conference on Machine Learning (ICML 2025). Post-conference manuscript

EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling

Theodoros Kouzelis, Ioannis Kakogeorgiou, Spyros Gidaris, Nikos Komodakis

机构 * Archimedes,Athena Reaserch Center, Greece（阿基米德、阿泰纳研究中心） ； National Technical University of Athens, Greece（雅典技术大学） ； University of Crete, Greece（克里特大学） 纠错

Comments ICML 2025

Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting

Jan Schuchardt, Mina Dalirrooyfard, Jed Guzelkabaagac, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann

机构 * Technical University of Munich \& Munich Data Science Institute ； Machine Learning Research, Morgan Stanley 纠错

Comments Accepted as ICML 2025 Spotlight

Enhancing Math Reasoning in Small-sized LLMs via Preview Difficulty-Aware Intervention

Xinhan Di, JoyJiaoW

机构 * Independent, Researcher, China（中国独立研究者） 纠错

Comments 7 pages, 1 table, accepted by SIM ICML@2025 Workshop

Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals

Junyan Liu, Arnab Maiti, Artin Tajdini, Kevin Jamieson, Lillian J. Ratliff

机构 * Paul G. Allen School of Computer Science \& Engineering, University of Washington, Seattle, USA ； Department of Electrical ； Computer Engineering, University of Washington, Seattle, USA 纠错

Comments To appear at ICML 2025

Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks

Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim, Suhong Moon, Hiroki Furuta, Gopala Anumanchipalli, Kurt Keutzer, Amir Gholami

机构 * University of Tokyo（东京大学） 纠错

Journal ref ICML 2025

Predictive Data Selection: The Data That Predicts Is the Data That Teaches

Kashun Shum, Yuzhen Huang, Hongjian Zou, Qi Ding, Yixuan Liao, Xiaoxin Chen, Qian Liu, Junxian He

机构 * Vivo AI Lab（Vivo人工智能实验室） 纠错

Comments ICML 2025

Emergent Response Planning in LLMs

Zhichen Dong, Zhanhui Zhou, Zhixuan Liu, Chao Yang, Chaochao Lu

机构 * Shanghai Artificial Intelligence Laboratory（上海人工智能实验室） 纠错

Comments Published at ICML 2025. Code available at: https://github.com/niconi19/Emergent-Response-Planning-in-LLMs

Journal ref Proceedings of the 42nd International Conference on Machine Learning (ICML 2025)

Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions

Yik Siu Chan, Narutatsu Ri, Yuxin Xiao, Marzyeh Ghassemi

机构 * Brown University（布朗大学） ； Columbia University（哥伦比亚大学） ； Massachusetts Institute of Technology（麻省理工学院） 纠错

Comments ICML 2025

Core Context Aware Transformers for Long Context Language Modeling

Yaofo Chen, Zeng You, Shuhai Zhang, Haokun Li, Yirui Li, Yaowei Wang, Mingkui Tan

机构 * South China University of Technology（华南理工大学） ； Peng Cheng Laboratory（鹏城实验室） ； Pazhou Laboratory（琶洲实验室） ； Harbin Institute of Technology（哈尔滨工业大学） ； Key Laboratory of Big Data and Intelligent Robot, Ministry of Education（教育部大数据与智能机器人重点实验室） 纠错

Comments Accepted for publication at ICML 2025

Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes

Jesse He, Helen Jenne, Herman Chau, Davis Brown, Mark Raugas, Sara Billey, Henry Kvinge

机构 * Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA, USA（哈利奇奥格鲁数据科学研究所，加州大学圣地亚哥分校） ； Pacific Northwest National Laboratory, Richland, WA, USA（太平洋西北国家实验室） ； Department of Mathematics, University of Washington, Seattle, WA, USA（数学系，华盛顿大学） 纠错

Comments ICML 2025. v3: Corrected typo in references

HiPPO-Prophecy: State-Space Models can Provably Learn Dynamical Systems in Context

Federico Arangath Joseph, Kilian Konstantin Haefeli, Noah Liniger, Caglar Gulcehre

机构 * ETH Zurich（苏黎世联邦理工学院） ； EPFL（苏黎世联邦理工学院） 纠错

Comments ICML 2024, Next Generation Sequence Modeling Architectures Workshop