AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. - Ruqi Zhang; A. Feder Cooper; Christopher De Sa (AISTATS, 2020)

Asymptotically Optimal Exact Minibatch Metropolis-Hastings. - Ruqi Zhang; A. Feder Cooper; Christopher De Sa (NeurIPS, 2020)

Regulating Accuracy-Efficiency Trade-Offs in Distributed Machine Learning Systems. - A. Feder Cooper; Karen Levy; Christopher De Sa (CoRR, 2020)

Where Is the Normative Proof? Assumptions and Contradictions in ML Fairness Research. - A. Feder Cooper (CoRR, 2020)

Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research. - A. Feder Cooper; Ellen Abrams; Na Na (AIES, 2021)

Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems. - A. Feder Cooper; Karen Levy; Christopher De Sa (EAAMO, 2021)

Hyperparameter Optimization Is Deceiving Us, and How to Stop It. - A. Feder Cooper; Yucheng Lu; Jessica Forde; Christopher De Sa (NeurIPS, 2021)

Emergent Unfairness: Normative Assumptions and Contradictions in Algorithmic Fairness-Accuracy Trade-Off Research. - A. Feder Cooper; Ellen Abrams (CoRR, 2021)

Hyperparameter Optimization Is Deceiving Us, and How to Stop It. - A. Feder Cooper; Yucheng Lu; Christopher De Sa (CoRR, 2021)

Model Selection's Disparate Impact in Real-World Deep Learning Applications. - Jessica Zosa Forde; A. Feder Cooper; Kweku Kwegyir-Aggrey; Chris De Sa; Michael Littman 002 (CoRR, 2021)

Tecnologica cosa: Modeling Storyteller Personalities in Boccaccio's Decameron. - A. Feder Cooper; Maria Antoniak; Christopher De Sa; Marilyn Migiel; David Mimno (CoRR, 2021)

Non-Determinism and the Lawlessness of Machine Learning Code. - A. Feder Cooper; Jonathan Frankle; Christopher De Sa (CSLAW, 2022)

Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning. - A. Feder Cooper; Emanuel Moss; Benjamin Laufer; Helen Nissenbaum (FAccT, 2022)

Making the Unaccountable Internet: The Changing Meaning of Accounting in the Early ARPANET. - A. Feder Cooper; Gili Vidan (FAccT, 2022)

Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects. - Benjamin Laufer; Sameer Jain; A. Feder Cooper; Jon M. Kleinberg; Hoda Heidari (FAccT, 2022)

Making the Unaccountable Internet: The Changing Meaning of Accounting in the Design of the Early Internet. - A. Feder Cooper; Gili Vidan (CoRR, 2022)

Non-Determinism and the Lawlessness of ML Code. - A. Feder Cooper; Jonathan Frankle; Christopher De Sa (CoRR, 2022)

Fast or Accurate? Governing Conflicting Goals in Highly Autonomous Vehicles. - A. Feder Cooper; Karen Levy (CoRR, 2022)

CD-GraB: Coordinating Distributed Example Orders for Provably Accelerated Training. - A. Feder Cooper; Wentao Guo; Khiem Pham; Tiancheng Yuan; Charlie Ruan; Yucheng Lu; Christopher De Sa (NeurIPS, 2023)

Variance, Self-Consistency, and Arbitrariness in Fair Classification. - A. Feder Cooper; Solon Barocas; Christopher De Sa; Siddhartha Sen 001 (CoRR, 2023)

Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain. - Katherine Lee; A. Feder Cooper; James Grimmelmann (CoRR, 2023)

CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images. - Aaron Gokaslan; A. Feder Cooper; Jasmine Collins; Landan Seguin; Austin Jacobson; Mihir Patel; Jonathan Frankle; Cory Stephenson; Volodymyr Kuleshov (CoRR, 2023)

Report of the 1st Workshop on Generative AI and Law. - 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; et al. (CoRR, 2023)

Scalable Extraction of Training Data from (Production) Language Models. - Milad Nasr; Nicholas Carlini; Jonathan Hayase; Matthew Jagielski; A. Feder Cooper; Daphne Ippolito; Christopher A. Choquette-Choo; Eric Wallace; Florian Tramèr; Katherine Lee (CoRR, 2023)

Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification. - A. Feder Cooper; Katherine Lee; Madiha Zahrah Choksi; Solon Barocas; Christopher De Sa; James Grimmelmann; Jon M. Kleinberg; Siddhartha Sen 001; Baobao Zhang (AAAI, 2024)

Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain (The Short Version). - Katherine Lee; A. Feder Cooper; James Grimmelmann (CSLAW, 2024)

Common Canvas: Open Diffusion Models Trained on Creative-Commons Images. - Aaron Gokaslan; A. Feder Cooper; Jasmine Collins; Landan Seguin; Austin Jacobson; Mihir Patel; Jonathan Frankle; Cory Stephenson; Volodymyr Kuleshov (CVPR, 2024)

Stealing part of a production language model. - Nicholas Carlini; Daniel Paleka; Krishnamurthy Dj Dvijotham; Thomas Steinke 002; Jonathan Hayase; A. Feder Cooper; Katherine Lee; Matthew Jagielski; Milad Nasr; Arthur Conmy; Eric Wallace; David Rolnick; et al. (ICML, 2024)

Position: Standardization of Behavioral Use Clauses is Necessary for the Adoption of Responsible Licensing of AI. - 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; Danish Contractor (ICML, 2024)

On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI. - 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; Danish Contractor (CoRR, 2024)

The Files are in the Computer: Copyright, Memorization, and Generative AI. - A. Feder Cooper; James Grimmelmann (CoRR, 2024)

Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale. - A. Feder Cooper (CoRR, 2024)

Evaluating Generative AI Systems is a Social Science Measurement Challenge. - Hanna M. Wallach; Meera A. Desai; Nicholas Pangakis; A. Feder Cooper; Angelina Wang; Solon Barocas; Alexandra Chouldechova; Chad Atalla; Su Lin Blodgett; Emily Corvi; P. Alex Dow; Jean Garcia-Gathright; et al. (CoRR, 2024)

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts. - Alexandra Chouldechova; Chad Atalla; Solon Barocas; A. Feder Cooper; Emily Corvi; P. Alex Dow; Jean Garcia-Gathright; Nicholas Pangakis; Stefanie Reed; Emily Sheng; Dan Vann; Matthew Vogel; et al. (CoRR, 2024)

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice. - 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; et al. (CoRR, 2024)

Scalable Extraction of Training Data from Aligned, Production Language Models. - Milad Nasr; Javier Rando; Nicholas Carlini; Jonathan Hayase; Matthew Jagielski; A. Feder Cooper; Daphne Ippolito; Christopher A. Choquette-Choo; Florian Tramèr; Katherine Lee (ICLR, 2025)

Measuring memorization in language models via probabilistic extraction. - Jamie Hayes; Marika Swanberg; Harsh Chaudhari; Itay Yona; Ilia Shumailov; Milad Nasr; Christopher A. Choquette-Choo; Katherine Lee; A. Feder Cooper (NAACL (Long Papers), 2025)

Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge. - 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; et al. (CoRR, 2025)

Extracting memorized pieces of (copyrighted) books from open-weight language models. - A. Feder Cooper; Aaron Gokaslan; Amy Cyphert; Christopher De Sa; Mark A. Lemley; Daniel E. Ho; Percy Liang (CoRR, 2025)

Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models. - Jamie Hayes; Ilia Shumailov; Christopher A. Choquette-Choo; Matthew Jagielski; Georgios Kaissis; Katherine Lee; Milad Nasr; Sahra Ghalebikesabi; Niloofar Mireshghallah; Meenatchi Sundaram Mutu Selva Annamalai; Igor Shilov; Matthieu Meeus; et al. (CoRR, 2025)

The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text. - 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; et al. (CoRR, 2025)

The California Report on Frontier AI Policy. - Rishi Bommasani; Scott R. Singer; Ruth E. Appel; Sarah Cen; A. Feder Cooper; Elena Cryst; Lindsey A. Gailmard; Ian Klaus; Meredith M. Lee; Inioluwa Deborah Raji; Anka Reuel; Drew Spence; et al. (CoRR, 2025)