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ORCIDA. Feder Cooper
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2020 – today
- 2024
[c15]A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon M. Kleinberg, Siddhartha Sen, Baobao Zhang:
Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification. AAAI 2024: 22004-22012- [c14]Katherine Lee
, A. Feder Cooper, James Grimmelmann:
Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain (The Short Version). CSLAW 2024: 48-63 - [c13]Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov:
Common Canvas: Open Diffusion Models Trained on Creative-Commons Images. CVPR 2024: 8250-8260 - [c12]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 - [c11]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:
Position: Standardization of Behavioral Use Clauses is Necessary for the Adoption of Responsible Licensing of AI. ICML 2024 - [i22]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:
On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI. CoRR abs/2402.05979 (2024) - [i21]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. CoRR abs/2403.06634 (2024) - [i20]A. Feder Cooper, James Grimmelmann:
The Files are in the Computer: Copyright, Memorization, and Generative AI. CoRR abs/2404.12590 (2024) - [i19]A. Feder Cooper:
Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale. CoRR abs/2406.09548 (2024) - 2023
- [c10]A. Feder Cooper, Wentao Guo, Khiem Pham, Tiancheng Yuan, Charlie Ruan, Yucheng Lu, Christopher De Sa:
CD-GraB: Coordinating Distributed Example Orders for Provably Accelerated Training. NeurIPS 2023 - [i18]A. Feder Cooper, Solon Barocas, Christopher De Sa, Siddhartha Sen:
Variance, Self-Consistency, and Arbitrariness in Fair Classification. CoRR abs/2301.11562 (2023) - [i17]Katherine Lee, A. Feder Cooper, James Grimmelmann:
Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain. CoRR abs/2309.08133 (2023) - [i16]Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov:
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images. CoRR abs/2310.16825 (2023) - [i15]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:
Report of the 1st Workshop on Generative AI and Law. CoRR abs/2311.06477 (2023) - [i14]Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, Katherine Lee:
Scalable Extraction of Training Data from (Production) Language Models. CoRR abs/2311.17035 (2023) - 2022
- [c9]A. Feder Cooper, Jonathan Frankle, Christopher De Sa:
Non-Determinism and the Lawlessness of Machine Learning Code. CSLAW 2022: 1-8 - [c8]Benjamin Laufer, Sameer Jain, A. Feder Cooper, Jon M. Kleinberg, Hoda Heidari:
Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects. FAccT 2022: 401-426 - [c7]A. Feder Cooper, Gili Vidan:
Making the Unaccountable Internet: The Changing Meaning of Accounting in the Early ARPANET. FAccT 2022: 726-742 - [c6]A. Feder Cooper, Emanuel Moss, Benjamin Laufer, Helen Nissenbaum:
Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning. FAccT 2022: 864-876 - [i13]A. Feder Cooper, Gili Vidan:
Making the Unaccountable Internet: The Changing Meaning of Accounting in the Design of the Early Internet. CoRR abs/2201.11884 (2022) - [i12]A. Feder Cooper, Benjamin Laufer, Emanuel Moss, Helen Nissenbaum:
Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning. CoRR abs/2202.05338 (2022) - [i11]Benjamin Laufer, Sameer Jain, A. Feder Cooper, Jon M. Kleinberg, Hoda Heidari:
Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects. CoRR abs/2206.06738 (2022) - [i10]A. Feder Cooper, Jonathan Frankle, Christopher De Sa:
Non-Determinism and the Lawlessness of ML Code. CoRR abs/2206.11834 (2022) - [i9]A. Feder Cooper, Karen Levy:
Fast or Accurate? Governing Conflicting Goals in Highly Autonomous Vehicles. CoRR abs/2208.02056 (2022) - 2021
- [c5]A. Feder Cooper, Ellen Abrams, Na Na:
Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research. AIES 2021: 46-54 - [c4]A. Feder Cooper, Karen Levy, Christopher De Sa:
Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems. EAAMO 2021: 4:1-4:11 - [c3]A. Feder Cooper, Yucheng Lu, Jessica Forde, Christopher De Sa:
Hyperparameter Optimization Is Deceiving Us, and How to Stop It. NeurIPS 2021: 3081-3095 - [i8]A. Feder Cooper, Ellen Abrams:
Emergent Unfairness: Normative Assumptions and Contradictions in Algorithmic Fairness-Accuracy Trade-Off Research. CoRR abs/2102.01203 (2021) - [i7]A. Feder Cooper, Yucheng Lu, Christopher De Sa:
Hyperparameter Optimization Is Deceiving Us, and How to Stop It. CoRR abs/2102.03034 (2021) - [i6]Jessica Zosa Forde, A. Feder Cooper, Kweku Kwegyir-Aggrey, Chris De Sa, Michael Littman:
Model Selection's Disparate Impact in Real-World Deep Learning Applications. CoRR abs/2104.00606 (2021) - [i5]A. Feder Cooper, Maria Antoniak, Christopher De Sa, Marilyn Migiel, David Mimno:
Tecnologica cosa: Modeling Storyteller Personalities in Boccaccio's Decameron. CoRR abs/2109.10506 (2021) - 2020
- [c2]Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. AISTATS 2020: 2142-2152 - [c1]Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
Asymptotically Optimal Exact Minibatch Metropolis-Hastings. NeurIPS 2020 - [i4]Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. CoRR abs/2003.00193 (2020) - [i3]Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
Asymptotically Optimal Exact Minibatch Metropolis-Hastings. CoRR abs/2006.11677 (2020) - [i2]A. Feder Cooper, Karen Levy, Christopher De Sa:
Regulating Accuracy-Efficiency Trade-Offs in Distributed Machine Learning Systems. CoRR abs/2007.02203 (2020) - [i1]A. Feder Cooper:
Where Is the Normative Proof? Assumptions and Contradictions in ML Fairness Research. CoRR abs/2010.10407 (2020)
Coauthor Index
aka: Chris De Sa
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last updated on 2024-10-04 20:58 CEST by the dblp team
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