A. Feder Cooper

According to our database, A. Feder Cooper authored at least 32 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale.

A. Feder Cooper

CoRR, 2024

The Files are in the Computer: Copyright, Memorization, and Generative AI.

A. Feder Cooper, James Grimmelmann

CoRR, 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

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Stealing part of a production language model.

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

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain (The Short Version).

Katherine Lee, A. Feder Cooper, James Grimmelmann

Proceedings of the Symposium on Computer Science and Law, 2024

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, Baobao Zhang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

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

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, 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, Elana Zeide

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

Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain.

Katherine Lee, A. Feder Cooper, James Grimmelmann

CoRR, 2023

Variance, Self-Consistency, and Arbitrariness in Fair Classification.

A. Feder Cooper, Solon Barocas, Christopher De Sa, Siddhartha Sen

CoRR, 2023

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Fast or Accurate? Governing Conflicting Goals in Highly Autonomous Vehicles.

A. Feder Cooper, Karen Levy

CoRR, 2022

Non-Determinism and the Lawlessness of ML Code.

A. Feder Cooper, Jonathan Frankle, Christopher De Sa

CoRR, 2022

Making the Unaccountable Internet: The Changing Meaning of Accounting in the Design of the Early Internet.

A. Feder Cooper, Gili Vidan

CoRR, 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

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Making the Unaccountable Internet: The Changing Meaning of Accounting in the Early ARPANET.

A. Feder Cooper, Gili Vidan

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning.

A. Feder Cooper, Emanuel Moss, Benjamin Laufer, Helen Nissenbaum

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Non-Determinism and the Lawlessness of Machine Learning Code.

A. Feder Cooper, Jonathan Frankle, Christopher De Sa

Proceedings of the 2022 Symposium on Computer Science and Law, 2022

2021

Tecnologica cosa: Modeling Storyteller Personalities in Boccaccio's Decameron.

A. Feder Cooper, Maria Antoniak, Christopher De Sa, Marilyn Migiel, David Mimno

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

CoRR, 2021

Hyperparameter Optimization Is Deceiving Us, and How to Stop It.

A. Feder Cooper, Yucheng Lu, Christopher De Sa

CoRR, 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, Jessica Forde, Christopher De Sa

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems.

A. Feder Cooper, Karen Levy, Christopher De Sa

Proceedings of the EAAMO 2021: ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, Virtual Event, USA, October 5, 2021

Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research.

A. Feder Cooper, Ellen Abrams, Na Na

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020

Where Is the Normative Proof? Assumptions and Contradictions in ML Fairness Research.

A. Feder Cooper

CoRR, 2020

Regulating Accuracy-Efficiency Trade-Offs in Distributed Machine Learning Systems.

A. Feder Cooper, Karen Levy, Christopher De Sa

CoRR, 2020

Asymptotically Optimal Exact Minibatch Metropolis-Hastings.

Ruqi Zhang, A. Feder Cooper, Christopher De Sa

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC.

Ruqi Zhang, A. Feder Cooper, Christopher De Sa

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020