| An introduction to computational learning theory MJ Kearns, U Vazirani MIT press, 1994 | 2060 | 1994 |
| Cryptographic limitations on learning Boolean formulae and finite automata M Kearns, L Valiant Journal of the ACM (JACM) 41 (1), 67-95, 1994 | 1202 | 1994 |
| Near-optimal reinforcement learning in polynomial time M Kearns, S Singh Machine learning 49 (2), 209-232, 2002 | 1105 | 2002 |
| Efficient noise-tolerant learning from statistical queries M Kearns Journal of the ACM (JACM) 45 (6), 983-1006, 1998 | 948 | 1998 |
| Graphical models for game theory M Kearns, ML Littman, S Singh arXiv preprint arXiv:1301.2281, 2013 | 739 | 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 | 694 | 2002 |
| Toward efficient agnostic learning MJ Kearns, RE Schapire, LM Sellie Machine Learning 17 (2-3), 115-141, 1994 | 623 | 1994 |
| 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 | 612 | 1989 |
| Learning in the presence of malicious errors M Kearns, M Li SIAM Journal on Computing 22 (4), 807-837, 1993 | 586 | 1993 |
| Algorithmic stability and sanity-check bounds for leave-one-out cross-validation M Kearns, D Ron Neural computation 11 (6), 1427-1453, 1999 | 569 | 1999 |
| 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 | 504 | 2021 |
| 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 | 454 | 2002 |
| On the complexity of teaching SA Goldman, MJ Kearns Journal of Computer and System Sciences 50 (1), 20-31, 1995 | 391 | 1995 |
| On the learnability of Boolean formulae M Kearns, M Li, L Pitt, L Valiant Proceedings of the nineteenth annual ACM symposium on Theory of computing …, 1987 | 382 | 1987 |
| Modeling the IT value paradox ME Thatcher, DE Pingry Communications of the ACM 50 (8), 41-45, 2007 | 371* | 2007 |
| 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 | 369 | 2018 |
| Cryptographic primitives based on hard learning problems A Blum, M Furst, M Kearns, RJ Lipton Annual International Cryptology Conference, 278-291, 1993 | 347 | 1993 |
| Nash Convergence of Gradient Dynamics in General-Sum Games. SP Singh, MJ Kearns, Y Mansour UAI, 541-548, 2000 | 341 | 2000 |
| An experimental study of the coloring problem on human subject networks M Kearns, S Suri, N Montfort science 313 (5788), 824-827, 2006 | 323 | 2006 |
| Fairness in learning: Classic and contextual bandits M Joseph, M Kearns, J Morgenstern, A Roth arXiv preprint arXiv:1605.07139, 2016 | 322 | 2016 |
