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ORCID42nd ICML 2025: Vancouver, BC, Canada - Position Paper Track
- Forty-second International Conference on Machine Learning, ICML 2025, Vancouver, BC, Canada, July 13-19, 2025 - Position Paper Track. OpenReview.net 2025
Accept (oral)
- D. Sculley, William Cukierski, Phil Culliton, Sohier Dane, Maggie Demkin, Ryan Holbrook, Addison Howard, Paul Mooney, Walter Reade, Meg Risdal, Nate Keating:
Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation. - Sanchaita Hazra, Bodhisattwa Prasad Majumder, Tuhin Chakrabarty:
Position: AI Safety should prioritize the Future of Work. - Ruth Elisabeth Appel:
Position: Generative AI Regulation Can Learn from Social Media Regulation. - Alan Jeffares, Mihaela van der Schaar:
Position: Not All Explanations for Deep Learning Phenomena Are Equally Valuable. - Jillian Fisher, Ruth Elisabeth Appel, Chan Young Park, Yujin Potter, Liwei Jiang, Taylor Sorensen, Shangbin Feng, Yulia Tsvetkov, Margaret E. Roberts, Jennifer Pan, Dawn Song, Yejin Choi:
Position: Political Neutrality in AI Is Impossible - But Here Is How to Approximate It. - Ahmed Alaa, Thomas Hartvigsen, Niloufar Golchini, Shiladitya Dutta, Frances Dean, Inioluwa Deborah Raji, Travis Zack:
Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity. - Sunayana Rane, Cyrus F. Kirkman, Graham Todd, Amanda L. Royka, Ryan M. C. Law, Erica A. Cartmill, Jacob Gates Foster:
Position: Principles of Animal Cognition to Improve LLM Evaluations. - Tobin South, Samuele Marro, Thomas Hardjono, Robert Mahari, Cedric Deslandes Whitney, Alan Chan, Alex Pentland:
Position: AI Agents Need Authenticated Delegation. - Bruno Kacper Mlodozeniec, David Krueger, Richard E. Turner:
Position: Probabilistic Modelling is Sufficient for Causal Inference. - Moming Duan, Mingzhe Du, Rui Zhao, Mengying Wang, Yinghui Wu, Nigel Shadbolt, Bingsheng He:
Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance. - Andrew Craig Cullen, Paul Montague, Sarah Monazam Erfani, Benjamin I. P. Rubinstein:
Position: Certified Robustness Does Not (Yet) Imply Model Security. - Jaeho Kim, Yunseok Lee, Seulki Lee:
Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards.
Accept (spotlight poster)
- Kirsten N. Morehouse, Siddharth Swaroop, Weiwei Pan:
Position: Rethinking LLM Bias Probing Using Lessons from the Social Sciences. - Shayne Longpre, Kevin Klyman, Ruth Elisabeth Appel, Sayash Kapoor, Rishi Bommasani, Michelle Sahar, Sean McGregor, Avijit Ghosh, Borhane Blili-Hamelin, Nathan Butters, Alondra Nelson, Amit Elazari, Andrew Sellars, Casey John Ellis, Dane Sherrets, Dawn Song, Harley Geiger, Ilona Cohen, Lauren McIlvenny, Madhulika Srikumar, Mark M. Jaycox, Markus Anderljung, Nadine Farid Johnson, Nicholas Carlini, Nicolas Miailhe, Nik Marda, Peter Henderson, Rebecca S. Portnoff, Rebecca Weiss, Victoria Westerhoff, Yacine Jernite, Rumman Chowdhury, Percy Liang, Arvind Narayanan:
Position: In-House Evaluation Is Not Enough. Towards Robust Third-Party Evaluation and Flaw Disclosure for General-Purpose AI. - John Hewitt, Robert Geirhos, Been Kim:
Position: We Can't Understand AI Using our Existing Vocabulary. - Sam Bowyer, Laurence Aitchison, Desi R. Ivanova:
Position: Don't Use the CLT in LLM Evals With Fewer Than a Few Hundred Datapoints. - Oliver Eberle, Thomas McGee, Hamza Giaffar, Taylor Whittington Webb, Ida Momennejad:
Position: We Need An Algorithmic Understanding of Generative AI. - Miriam Doh, Benedikt Höltgen, Piera Riccio, Nuria Oliver:
Position: The Categorization of Race in ML is a Flawed Premise. - Andrew Gordon Wilson:
Position: Deep Learning is Not So Mysterious or Different. - Giovanni Luca Marchetti, Vahid Shahverdi, Stefano Mereta, Matthew Trager, Kathlén Kohn:
Position: Algebra Unveils Deep Learning - An Invitation to Neuroalgebraic Geometry. - Andy K. Zhang, Kevin Klyman, Yifan Mai, Yoav Levine, Yian Zhang, Rishi Bommasani, Percy Liang:
Position: Language model developers should report train-test overlap. - Seungwook Han, Jyothish Pari, Samuel J. Gershman, Pulkit Agrawal:
Position: General Intelligence Requires Reward-based Pretraining. - Kevin L. Wei, Patricia Paskov, Sunishchal Dev, Michael J. Byun, Anka Reuel, Xavier Roberts-Gaal, Rachel Calcott, Evie Coxon, Chinmay Deshpande:
Position: Human Baselines in Model Evaluations Need Rigor and Transparency (With Recommendations & Reporting Checklist). - Kaiyu Yang, Gabriel Poesia, Jingxuan He, Wenda Li, Kristin E. Lauter, Swarat Chaudhuri, Dawn Song:
Position: Formal Mathematical Reasoning - A New Frontier in AI.
Accept (poster)
- Samuel Müller, Arik Reuter, Noah Hollmann, David Rügamer, Frank Hutter:
Position: The Future of Bayesian Prediction Is Prior-Fitted. - Jan Kulveit, Raymond Douglas, Nora Ammann, Deger Turan, David Krueger, David Duvenaud:
Position: Humanity Faces Existential Risk from Gradual Disempowerment. - Sunayana Rane:
Position: AI's growing due process problem. - Indradyumna Roy, Saswat Meher, Eeshaan Jain, Soumen Chakrabarti, Abir De:
Position: Graph Matching Systems Deserve Better Benchmarks. - Golnaz Mesbahi, Parham Mohammad Panahi, Olya Mastikhina, Steven Tang, Martha White, Adam White:
Position: Lifetime tuning is incompatible with continual reinforcement learning. - Sarah Hartman, Cheng Soon Ong, Julia Powles, Petra Kuhnert:
Position: We Need Responsible, Application-Driven (RAD) AI Research. - Ma Kimmel, Mueed Ur Rehman, Yonatan Bisk, Gary K. Fedder:
Position: You Can't Manufacture a NeRF. - Alex Gu, Naman Jain, Wen-Ding Li, Manish Shetty, Kevin Ellis, Koushik Sen, Armando Solar-Lezama:
Position: Future Research and Challenges Remain Towards AI for Software Engineering. - Borhane Blili-Hamelin, Christopher Graziul, Leif Hancox-Li, Hananel Hazan, El-Mahdi El-Mhamdi, Avijit Ghosh, Katherine A. Heller, Jacob Metcalf, Fabricio Murai, Eryk Salvaggio, Andrew Smart, Todd Snider, Mariame Tighanimine, Talia Ringer, Margaret Mitchell, Shiri Dori-Hacohen:
Position: Stop treating `AGI' as the north-star goal of AI research. - Ming Jin, Hyunin Lee:
Position: AI Safety Must Embrace an Antifragile Perspective. - Frithjof Gressmann, Ashley Chen, Lily Hexuan Xie, Nancy M. Amato, Lawrence Rauchwerger:
Position: It Is Time We Test Neural Computation In Vitro. - Tennison Liu, Mihaela van der Schaar:
Position: Truly Self-Improving Agents Require Intrinsic Metacognitive Learning. - Yunke Wang, Yanxi Li, Chang Xu:
Position: AI Scaling: From Up to Down and Out. - Nikhil Kandpal, Colin Raffel:
Position: The Most Expensive Part of an LLM *should* be its Training Data. - Patrik Reizinger, Randall Balestriero, David A. Klindt, Wieland Brendel:
Position: An Empirically Grounded Identifiability Theory Will Accelerate Self Supervised Learning Research. - Yucen Lily Li, Daohan Lu, Polina Kirichenko, Shikai Qiu, Tim G. J. Rudner, C. Bayan Bruss, Andrew Gordon Wilson:
Position: Supervised Classifiers Answer the Wrong Questions for OOD Detection. - Elliot Meyerson, Xin Qiu:
Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives. - Audrey Poinsot, Panayiotis Panayiotou, Alessandro Ferreira Leite, Nicolas Chesneau, Özgür Simsek, Marc Schoenauer:
Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption. - Abeer Badawi, Md. Tahmid Rahman Laskar, Jimmy Huang, Shaina Raza, Elham Dolatabadi:
Position: Beyond Assistance - Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care. - Yash Goel, Ayan Sengupta, Tanmoy Chakraborty:
Position: Enough of Scaling LLMs! Lets Focus on Downscaling. - Paul Youssef, Zhixue Zhao, Daniel Braun, Jörg Schlötterer, Christin Seifert:
Position: Editing Large Language Models Poses Serious Safety Risks. - Yuhe Guo, Huayi Tang, Jiahong Ma, Hongteng Xu, Zhewei Wei:
Position: Spectral GNNs Rely Less on Graph Fourier Basis than Conceived. - Sebastin Santy, Prasanta Bhattacharya, Manoel Horta Ribeiro, Kelsey R. Allen, Sewoong Oh:
Position: When Incentives Backfire, Data Stops Being Human. - Matthew Riemer, Zahra Ashktorab, Djallel Bouneffouf, Payel Das, Miao Liu, Justin D. Weisz, Murray Campbell:
Position: Theory of Mind Benchmarks are Broken for Large Language Models. - Andreas Haupt, Erik Brynjolfsson:
Position: AI Should Not Be An Imitation Game: Centaur Evaluations. - Hanqi Yan, Linhai Zhang, Jiazheng Li, Zhenyi Shen, Yulan He:
Position: LLMs Need a Bayesian Meta-Reasoning Framework for More Robust and Generalizable Reasoning. - Sarah Meiklejohn, Hayden Blauzvern, Mihai Maruseac, Spencer Schrock, Laurent Simon, Ilia Shumailov:
Position: Machine Learning Models Have a Supply Chain Problem. - Matej Piculin, Bernarda Petek, Irena Ograjensek, Erik Strumbelj:
Position: Explainable AI Cannot Advance Without Better User Studies. - David A. Danhofer, Davide D'Ascenzo, Rafael Dubach, Tomaso A. Poggio:
Position: A Theory of Deep Learning Must Include Compositional Sparsity. - Hanna M. Wallach, Meera A. Desai, A. Feder Cooper, Angelina Wang, Chad Atalla, Solon Barocas, Su Lin Blodgett, Alexandra Chouldechova, Emily Corvi, P. Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Nicholas J. Pangakis, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, Abigail Z. Jacobs:
Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge. - Aviv Ovadya, Kyle Redman, Luke Thorburn, Quan Ze Chen, Oliver Smith, Flynn Devine, Andrew Konya, Smitha Milli, Manon Revel, Kevin Feng, Amy X. Zhang, Bilva Chandra, Michiel A. Bakker, Atoosa Kasirzadeh:
Position: Democratic AI is Possible. The Democracy Levels Framework Shows How It Might Work. - Sebastian Bordt, Eric Raidl, Ulrike von Luxburg:
Position: Rethinking Explainable Machine Learning as Applied Statistics. - Yedi Zhang, Yufan Cai, Xinyue Zuo, Xiaokun Luan, Kailong Wang, Zhe Hou, Yifan Zhang, Zhiyuan Wei, Meng Sun, Jun Sun, Jing Sun, Jin Song Dong:
Position: Trustworthy AI Agents Require the Integration of Large Language Models and Formal Methods. - Yanzhou Pan, Jiayi Chen, Jiamin Chen, Zhaozhuo Xu, Denghui Zhang:
Position: Iterative Online-Offline Joint Optimization is Needed to Manage Complex LLM Copyright Risks. - Judy Hanwen Shen:
Position: Societal Impacts Research Requires Benchmarks for Creative Composition Tasks. - Lionel Wong, Ayman Ali, Raymond M. Xiong, Zejiang Shen, Yoon Kim, Monica Agrawal:
Position: Retrieval-augmented systems can be dangerous medical communicators. - Yan Shvartzshnaider, Vasisht Duddu:
Position: Contextual Integrity is Inadequately Applied to Language Models. - Serena Booth:
Position: Strong Consumer Protection is an Inalienable Defense for AI Safety in the United States. - Rashid Mushkani, Hugo Berard, Allison Cohen, Shin Koseki:
Position: The Right to AI. - Jacy Reese Anthis, Ryan Liu, Sean M. Richardson, Austin C. Kozlowski, Bernard Koch, Erik Brynjolfsson, James A. Evans, Michael S. Bernstein:
Position: LLM Social Simulations Are a Promising Research Method. - Yoonsoo Nam, Seok Hyeong Lee, Clémentine Carla Juliette Dominé, Yeachan Park, Charles London, Wonyl Choi, Niclas Alexander Göring, Seungjai Lee:
Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking). - Yan Zhuang, Qi Liu, Zachary A. Pardos, Patrick C. Kyllonen, Jiyun Zu, Zhenya Huang, Shijin Wang, Enhong Chen:
Position: AI Evaluation Should Learn from How We Test Humans. - Min-Hsuan Yeh, Jeffrey Wang, Xuefeng Du, Seongheon Park, Leitian Tao, Shawn Im, Yixuan Li:
Position: Challenges and Future Directions of Data-Centric AI Alignment. - Ossi Räisä, Boris van Breugel, Mihaela van der Schaar:
Position: All Current Generative Fidelity and Diversity Metrics are Flawed. - Michael Kirchhof, Gjergji Kasneci, Enkelejda Kasneci:
Position: Uncertainty Quantification Needs Reassessment for Large Language Model Agents. - Sayash Kapoor, Noam Kolt, Seth Lazar:
Position: Build Agent Advocates, Not Platform Agents. - Simone Drago, Marco Mussi, Alberto Maria Metelli:
Position: Constants are Critical in Regret Bounds for Reinforcement Learning. - Jing Yang:
Position: The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process. - Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Müller, Jan Tönshoff, Antoine Siraudin, Viktor Zaverkin, Michael M. Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, Christopher Morris:
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks.
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