| The Algorithmic Foundations of Differential Privacy C Dwork, A Roth Foundations and Trends® in Theoretical Computer Science 9 (3-4), 211-407, 2014 | 9027 | 2014 |
| Fairness in criminal justice risk assessments: The state of the art R Berk, H Heidari, S Jabbari, M Kearns, A Roth Sociological Methods & Research 50 (1), 3-44, 2021 | 1204 | 2021 |
| Preventing fairness gerrymandering: Auditing and learning for subgroup fairness M Kearns, S Neel, A Roth, ZS Wu International conference on machine learning, 2564-2572, 2018 | 940 | 2018 |
| A snapshot of the frontiers of fairness in machine learning A Chouldechova, A Roth Communications of the ACM 63 (5), 82-89, 2020 | 913* | 2020 |
| A learning theory approach to non-interactive database privacy A Blum, K Ligett, A Roth Journal of the ACM (JACM) 60 (2), 12:1-12:25, 2013 | 903 | 2013 |
| The ethical algorithm: The science of socially aware algorithm design M Kearns, A Roth Oxford University Press, 2019 | 580 | 2019 |
| Fairness in learning: Classic and contextual bandits M Joseph, M Kearns, JH Morgenstern, A Roth Advances in Neural Information Processing Systems, 325-333, 2016 | 546 | 2016 |
| Gaussian differential privacy J Dong, A Roth, WJ Su Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2022 | 500 | 2022 |
| Selling privacy at auction A Ghosh, A Roth Proceedings of the 12th ACM conference on Electronic commerce, 199-208, 2011 | 484 | 2011 |
| Preserving statistical validity in adaptive data analysis C Dwork, V Feldman, M Hardt, T Pitassi, O Reingold, AL Roth Proceedings of the forty-seventh annual ACM symposium on Theory of computing …, 2015 | 422 | 2015 |
| Differential privacy: An economic method for choosing epsilon J Hsu, M Gaboardi, A Haeberlen, S Khanna, A Narayan, BC Pierce, ... 2014 IEEE 27th Computer Security Foundations Symposium, 398-410, 2014 | 406 | 2014 |
| The reusable holdout: Preserving validity in adaptive data analysis C Dwork, V Feldman, M Hardt, T Pitassi, O Reingold, A Roth Science 349 (6248), 636-638, 2015 | 402 | 2015 |
| A convex framework for fair regression R Berk, H Heidari, S Jabbari, M Joseph, M Kearns, J Morgenstern, S Neel, ... arXiv preprint arXiv:1706.02409, 2017 | 397 | 2017 |
| Interactive privacy via the median mechanism A Roth, T Roughgarden Proceedings of the 42nd ACM symposium on Theory of computing, 765-774, 2010 | 327 | 2010 |
| Differentially private combinatorial optimization A Gupta, K Ligett, F McSherry, A Roth, K Talwar Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete …, 2010 | 295 | 2010 |
| Generalization in adaptive data analysis and holdout reuse C Dwork, V Feldman, M Hardt, T Pitassi, O Reingold, A Roth Advances in neural information processing systems 28, 2015 | 264 | 2015 |
| Descent-to-delete: Gradient-based methods for machine unlearning S Neel, A Roth, S Sharifi-Malvajerdi Algorithmic Learning Theory, 931-962, 2021 | 251 | 2021 |
| Fairness in reinforcement learning S Jabbari, M Joseph, M Kearns, J Morgenstern, A Roth International conference on machine learning, 1617-1626, 2017 | 249* | 2017 |
| An empirical study of rich subgroup fairness for machine learning M Kearns, S Neel, A Roth, ZS Wu Proceedings of the conference on fairness, accountability, and transparency …, 2019 | 234 | 2019 |
| Constrained non-monotone submodular maximization: Offline and secretary algorithms A Gupta, A Roth, G Schoenebeck, K Talwar International Workshop on Internet and Network Economics, 246-257, 2010 | 232 | 2010 |
Michael KearnsProfessor of Computer Science, University of Pennsylvania在 cis.upenn.edu 的電子郵件地址已通過驗證
