| An introduction to computational learning theory MJ Kearns, U Vazirani MIT press, 1994 | 2537 | 1994 |
| Near-optimal reinforcement learning in polynomial time M Kearns, S Singh Machine learning 49 (2), 209-232, 2002 | 1530 | 2002 |
| Cryptographic limitations on learning boolean formulae and finite automata M Kearns, L Valiant Journal of the ACM (JACM) 41 (1), 67-95, 1994 | 1524 | 1994 |
| 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 | 1518 | 2021 |
| Efficient noise-tolerant learning from statistical queries M Kearns Journal of the ACM (JACM) 45 (6), 983-1006, 1998 | 1273 | 1998 |
| 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 | 1166 | 2018 |
| Graphical models for game theory M Kearns, ML Littman, S Singh arXiv preprint arXiv:1301.2281, 2013 | 865 | 2013 |
| A sparse sampling algorithm for near-optimal planning in large Markov decision processes M Kearns, Y Mansour, AY Ng Machine learning 49 (2), 193-208, 2002 | 842 | 2002 |
| Toward efficient agnostic learning MJ Kearns, RE Schapire, LM Sellie Proceedings of the fifth annual workshop on Computational learning theory …, 1992 | 762 | 1992 |
| Algorithmic stability and sanity-check bounds for leave-one-out cross-validation M Kearns, D Ron Proceedings of the tenth annual conference on Computational learning theory …, 1997 | 744 | 1997 |
| The ethical algorithm: The science of socially aware algorithm design M Kearns, A Roth Oxford University Press, 2019 | 706 | 2019 |
| Learning in the presence of malicious errors M Kearns, M Li Proceedings of the twentieth annual ACM symposium on Theory of computing …, 1988 | 683 | 1988 |
| A general lower bound on the number of examples needed for learning A Ehrenfeucht, D Haussler, M Kearns, L Valiant Information and Computation 82 (3), 247-261, 1989 | 678 | 1989 |
| Fairness in learning: Classic and contextual bandits M Joseph, M Kearns, JH Morgenstern, A Roth Advances in neural information processing systems 29, 2016 | 636 | 2016 |
| Optimizing dialogue management with reinforcement learning: Experiments with the NJFun system S Singh, D Litman, M Kearns, M Walker Journal of Artificial Intelligence Research 16, 105-133, 2002 | 522 | 2002 |
| Cryptographic primitives based on hard learning problems A Blum, M Furst, M Kearns, RJ Lipton Annual international cryptology conference, 278-291, 1993 | 513 | 1993 |
| On the complexity of teaching SA Goldman, MJ Kearns Journal of Computer and System Sciences 50 (1), 20-31, 1995 | 483 | 1995 |
| 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 | 482 | 2017 |
| Reinforcement learning for optimized trade execution Y Nevmyvaka, Y Feng, M Kearns Proceedings of the 23rd international conference on Machine learning, 673-680, 2006 | 442 | 2006 |
| The computational complexity of machine learning MJ Kearns MIT press, 1990 | 419 | 1990 |
