Scholarship

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Full list

*Denotes co-first author or equal contribution; informal writing here

• Jamie Hayes*, Ilia Shumailov, Christopher A. Choquette-Choo, Matthew Jagielski, George Kaissis, Katherine Lee, Milad Nasr, Sahra Ghalebikesabi, Niloofar Mireshghallah, Meenatchi Sundaram Mutu Selva Annamalai, Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye, Franziska Boenisch, Adam Dziedzic, and A. Feder Cooper*. "Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models." [bibtex | arxiv] arXiv


• A. Feder Cooper, Aaron Gokaslan, Amy B. Cyphert, Christopher De Sa, Mark A. Lemley, Daniel E. Ho, and Percy Liang. "Extracting memorized pieces of (copyrighted) books from open-weight language models." [bibtex | arxiv] arXiv


• Nikhil Kandpal*, Brian Lester*, Colin Raffel*, Sebastian Majstorovic, Stella Biderman, Baber Abbasi, Luca Soldaini, Enrico Shippole, A. Feder Cooper, Aviya Skowron, John Kirchenbauer, Shayne Longpre, Lintang Sutawika, Alon Albalak, Zhenlin Xu, Guilherme Penedo, Loubna Ben Allal, Elie Bakouch, John David Pressman, Honglu Fan, Dashiell Stander, Guangyu Song, Aaron Gokaslan, Tom Goldstein, Brian R. Bartoldson, Bhavya Kailkhura, and Tyler Murray. "The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text." [bibtex | arxiv] arXiv


• Jamie Hayes, Marika Swanberg, Harsh Chaudhari, Itay Yona, Ilia Shumailov, Milad Nasr, Christopher A. Choquette-Choo, Katherine Lee, and A. Feder Cooper. "Measuring memorization in language models via probabilistic extraction." NAACL 2025. [bibtex | arxiv | proceedings | talk] Proceedings


• A. Feder Cooper* and James Grimmelmann*. "The Files are in the Computer: On Copyright, Memorization, and Generative AI." Forthcoming, Chicago-Kent Law Review, 2025 (written in 2024). [bibtex | arxiv | ssrn | journal] Journal


• A. Feder Cooper*, Katherine Lee*, and James Grimmelmann*. "Talkin’ ‘Bout AI Generation: Copyright and the Generative-AI Supply Chain." Forthcoming, Journal of the Copyright Society, 2025 (written in 2023). [bibtex | arxiv | ssrn | journal] Journal


• Milad Nasr*,Javier Rando*, Nicholas Carlini*, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Florian Tramèr, and Katherine Lee. "Scalable Extraction of Training Data from Aligned, Production Language Models." ICLR 2025. [bibtex | OpenReview] Proceedings


• A. Feder Cooper*, Christopher A. Choquette-Choo*, Miranda Bogen*, Matthew Jagielski*, Katja Filippova*, Ken Ziyu Liu*, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Niloofar Mireshghallah, Ilia Shumailov, Eleni Triantafillou, Peter Kairouz, Nicole Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, Amy Cyphert, Mark Lemley, danah boyd, Jennifer Wortman Vaughan, Miles Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna Wallach, Nicolas Papernot, and Katherine Lee. "Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice." 2024. [bibtex | arxiv | ssrn] arXiv


• Hanna Wallach, Meera Desai, Nicholas J. Pangakis, A. Feder Cooper, Angelina Wang, Solon Barocas, Alexandra Chouldechova, Chad Atalla, Su Lin Blodgett, Emily Corvi, P. Alexander Dow, Jean Garcia-Gathright, Alexandra Olteanu, Stefanie Reed, Emily Shang, Dan Vann, Jenn Wortman Vaughan, Matthew Vogel, Hannah Washington, and Abigail Z. Jacobs. "Evaluating Generative AI Systems is a Social Science Measurement Challenge." Workshop on Evaluating Evaluations at NeurIPS '24. [bibtex | arxiv] Workshop Oral


• Alexandra Chouldechova, Chad Atalla, Solon Barocas, A. Feder Cooper, Emily Corvi, P. Alex Dow, Jean Garcia-Gathright, Nicholas J Pangakis, Stefanie Reed, Emily Sheng, Dan Vann, Matthew Vogel, Hannah Washington, and Hanna Wallach. "A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts."" Statistical Frontiers in LLMs and Foundation Models at NeurIPS '24. Workshop


• A. Feder Cooper. "Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale." Cornell University, 2024. [bibtex | arxiv | ssrn | proquest] Ph.D. Thesis


• A. Feder Cooper. "Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale (The Short Version)." 2024. [bibtex | ssrn] Blog


• Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr. "Stealing Part of a Production Language Model." ICML 2024. [bibtex | arxiv | proceedings] Proceedings Best Paper Award


• Daniel McDuff, Tim Korjakow, Scott Cambo, Jesse Josua Benjamin, Jenny Lee, Yacine Jernite, Carlos Muñoz Ferrandis, Aaron Gokaslan, Alek Tarkowski, Joseph Lindley, A. Feder Cooper, and Danish Contractor "On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI." ICML 2024. [bibtex | arxiv | proceedings] Proceedings


• Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, and Volodymyr Kuleshov. "CommonCanvas: Open Diffusion Models Trained on Creative-Commons Images." CVPR 2024. [bibtex | arxiv | proceedings] Proceedings


• A. Feder Cooper*, Katherine Lee*, and James Grimmelmann*. "Talkin’ ‘Bout AI Generation: Copyright and the Generative-AI Supply Chain (The Short Version)." ACM CSLAW, 2024. [bibtex | ssrn | proceedings] Proceedings Long Presentation


• A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon Kleinberg, Siddhartha Sen, and Baobao Zhang. "Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification." AAAI 2024 (written in 2022). [bibtex | arxiv | proceedings] Proceedings Best Student Paper Honorable Mention


• Milad Nasr*, Nicholas Carlini*, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, and Katherine Lee. "Scalable Extraction of Training Data from (Production) Language Models." 2023. [bibtex | arxiv] arXiv


• A. Feder Cooper*, Wentao Guo*, Khiem Pham*, Tiancheng Yuan, Charlie F. Ruan, Yucheng Lu, and Christopher De Sa. "Coordinating Distributed Example Orders for Provably Accelerated Training." NeurIPS 2023. [bibtex | arxiv | proceedings] Proceedings


• Kweku Kwegyir-Aggrey, A. Feder Cooper, Jessica Dai, John Dickerson, Keegan Hines, and Suresh Venkatasubramanian. "Repairing Regressors for Fair Classification at Any Decision Threshold." Algorithmic Fairness through the Lens of Time Workshop at NeurIPS 2023. [bibtex] Workshop Oral


• A. Feder Cooper*, Katherine Lee*, James Grimmelmann*, Daphne Ippolito*, Christopher Callison-Burch, Christopher A. Choquette-Choo, Niloofar Mireshghallah, Miles Brundage, David Mimno, Madiha Zahrah Choksi, Jack M. Balkin, Nicholas Carlini, Christopher De Sa, Jonathan Frankle, Deep Ganguli, Bryant Gipson, Andres Guadamuz, Swee Leng Harris, Abigail Z. Jacobs, Elizabeth Joh, Gautam Kamath, Mark Lemley, Cass Matthews, Christine McLeavey, Corynne McSherry, Milad Nasr, Paul Ohm, Adam Roberts, Tom Rubin, Pamela Samuelson, Ludwig Schubert, Kristen Vaccaro, Luis Villa, Felix Wu, and Elana Zeide. "Report of the 1st Workshop on Generative AI and Law." 2023. [bibtex | arxiv] arXiv


• A. Feder Cooper*, Katherine Lee*, James Grimmelmann, and Daphne Ippolito. "AI and Law: The Next Generation (An explainer series)." 2023. [bibtex | ssrn] Blog


• A. Feder Cooper, Katherine Lee, Madiha Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon Kleinberg, Siddhartha Sen, and Baobao Zhang. "Distribution Justice: Variance, Uncertainty, and Rules in Machine Learning and Law." Data (Re)Makes the World Conference, Information Society Project at Yale Law School; Privacy Law Scholars Conference. 2023. Workshop


• A. Feder Cooper, Jonathan Frankle, and Christopher De Sa. "Non-Determinism and the Lawlessness of Machine Learning Code." CSLAW 2022. [bibtex | arxiv] Proceedings Long Presentation


• A. Feder Cooper and Karen Levy. "Fast or Accurate? Governing Conflicting Goals in Highly Autonomous Vehicles." Colorado Technology Law Journal, Vol. 20, 2022. [bibtex | arxiv | ssrn] Journal


• A. Feder Cooper*, Emanuel Moss*, Benjamin Laufer, and Helen Nissenbaum. "Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning." FAccT 2022. [bibtex | arxiv | proceedings] Proceedings


• A. Feder Cooper and Gili Vidan. "Making the Unaccountable Internet: The Changing Meaning of Accounting in the Early ARPANET." FAccT 2022. [bibtex | arxiv | proceedings] Proceedings


• Benjamin Laufer, A. Feder Cooper*, Sameer Jain*, Jon Kleinberg, and Hoda Heidari. "Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects." FAccT 2022. [bibtex | arxiv | proceedings] Proceedings


• A. Feder Cooper, Solon Barocas, Karen Levy, and Gili Vidan. "‘We have met the enemy and it is us’: Debating the ethics of computing in the pages of CACM." SIGCIS 2022. Workshop


• A. Feder Cooper, Yucheng Lu, Jessica Zosa Forde, and Christopher De Sa. "Hyperparameter Optimization Is Deceiving Us, and How to Stop It." NeurIPS 2021. [bibtex | arxiv | proceedings] Proceedings


• A. Feder Cooper, Maria Antoniak, Christopher De Sa, Marilyn Migiel, and David Mimno. "‘Tecnologica cosa’: Modeling Storyteller Personalities in Boccaccio’s Decameron." SIGHUM Workshop at EMNLP 2021. [bibtex | arxiv | proceedings] Proceedings


• A. Feder Cooper, Karen Levy, and Christopher De Sa. "Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems." EAAMO 2021. [bibtex | arxiv | proceedings] Proceedings Oral


• A. Feder Cooper and Ellen Abrams. "Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research." AIES 2021. [bibtex | arxiv | proceedings] Proceedings Oral


• A. Feder Cooper*, Jessica Zosa Forde*, Kweku Kwegyir-Aggrey, Christopher De Sa, and Michael Littman. "Model Selection's Disparate Impact in Real-World Deep Learning Applications." Science of Deep Learning Workshop at ICLR 2021. [bibtex | arxiv] Workshop Oral


• A. Feder Cooper*, Ruqi Zhang*, and Christopher De Sa. "Asymptotically Optimal Exact Minibatch Metropolis-Hastings." NeurIPS 2020. [bibtex | arxiv | proceedings] Proceedings Spotlight


• Ruqi Zhang, A. Feder Cooper, and Christopher De Sa. "AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC." AISTATS 2020. [bibtex | arxiv | proceedings] Proceedings


• A. Feder Cooper. "Imperfection is the Norm: A Computer Systems Perspective on IoT and Enforcement." (Im)Perfect Enforcement Conference, Information Society Project at Yale Law School. 2019. Workshop Plenary Session