*Equal contribution
Evaluating large-scale generative systems
- 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, 2024. [bibtex] Journal
- 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]
- Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, and Volodymyr Kuleshov. "CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images." Workshop on Diffusion Models and Workshop on ML for Creativity and Design at NeurIPS 2023. Under conference submission. [bibtex] Workshop
- 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] Technical Report
- A. Feder Cooper*, Katherine Lee*, James Grimmelmann, and Daphne Ippolito. "AI and Law: The Next Generation (An explainer series)." 2023. [bibtex] Blog
Reliable, scalable ML (MLSys)
- A. Feder Cooper*, Wentao Guo*, Khiem Pham*, Tiancheng Yuan, Charlie F. Ruan, Yucheng Lu, and Christopher De Sa. "CD-GraB: Coordinating Distributed Example Orders for Provably Accelerated Training." Forthcoming, NeurIPS 2023. [bibtex] Proceedings
- A. Feder Cooper*, Ruqi Zhang*, and Christopher De Sa. "Asymptotically Optimal Exact Minibatch Metropolis-Hastings." NeurIPS 2020. [bibtex] Proceedings Spotlight
- Ruqi Zhang, A. Feder Cooper, and Christopher De Sa. "AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC." AISTATS 2020. [bibtex] Proceedings
- A. Feder Cooper, Karen Levy, and Christopher De Sa. "Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems." EAAMO 2021. [bibtex] Proceedings Oral
- A. Feder Cooper and Karen Levy. "Fast or Accurate? Governing Conflicting Goals in Highly Autonomous Vehicles." Colorado Technology Law Journal, Vol. 20, 2022. [bibtex] Journal
- 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
Arbitrariness in automated decision systems
- 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] Proceedings
- A. Feder Cooper, Katherine Lee, Madiha Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon Kleinberg, Siddhartha Sen, and Baobao Zhang. "Arbitrariness and Prediction: The Confounding Role of Variance in Fair Classification." Workshop on Algorithmic Fairness through the Lens of Time at NeurIPS 2023. Under conference submission. [bibtex] Workshop
- 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] Workshop Oral
- A. Feder Cooper, Jonathan Frankle, and Christopher De Sa. "Non-Determinism and the Lawlessness of Machine Learning Code." CSLAW 2022. [bibtex] Proceedings
- 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
Social values in computing
- 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] 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." Workshop on Optimal Transport and Machine Learning; Workshop on Algorithmic Fairness through the Lens of Time at NeurIPS 2023. Under conference submission. [bibtex] Workshop Oral
- A. Feder Cooper and Ellen Abrams. "Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research." AIES 2021. [bibtex] Proceedings Oral
- A. Feder Cooper and Gili Vidan. "Making the Unaccountable Internet: The Changing Meaning of Accounting in the Early ARPANET." FAccT 2022. [bibtex] 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
- 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] Proceedings
Other projects