Michael Kearns
TitleCited byYear
An introduction to computational learning theory
MJ Kearns, UV Vazirani, U Vazirani
MIT press, 1994
17981994
Cryptographic limitations on learning Boolean formulae and finite automata
M Kearns, L Valiant
Journal of the ACM (JACM) 41 (1), 67-95, 1994
10491994
Near-optimal reinforcement learning in polynomial time
M Kearns, S Singh
Machine learning 49 (2-3), 209-232, 2002
8382002
Efficient noise-tolerant learning from statistical queries
M Kearns
Journal of the ACM (JACM) 45 (6), 983-1006, 1998
7811998
Graphical models for game theory
M Kearns, ML Littman, S Singh
arXiv preprint arXiv:1301.2281, 2013
6592013
A sparse sampling algorithm for near-optimal planning in large Markov decision processes
M Kearns, Y Mansour, AY Ng
Machine learning 49 (2-3), 193-208, 2002
5792002
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
5461989
Toward efficient agnostic learning
MJ Kearns, RE Schapire, LM Sellie
Machine Learning 17 (2-3), 115-141, 1994
5201994
Learning in the presence of malicious errors
M Kearns, M Li
SIAM Journal on Computing 22 (4), 807-837, 1993
5061993
Algorithmic stability and sanity-check bounds for leave-one-out cross-validation
M Kearns, D Ron
Neural computation 11 (6), 1427-1453, 1999
4681999
Efficient distribution-free learning of probabilistic concepts
MJ Kearns, RE Schapire
Journal of Computer and System Sciences 48 (3), 464-497, 1994
4401994
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
3832002
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
3731987
On the complexity of teaching
SA Goldman, MJ Kearns
3411992
Modeling the IT value paradox
ME Thatcher, DE Pingry
Communications of the ACM 50 (8), 41-45, 2007
304*2007
Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension
D Haussler, M Kearns, RE Schapire
Machine learning 14 (1), 83-113, 1994
2971994
Nash Convergence of Gradient Dynamics in General-Sum Games.
SP Singh, MJ Kearns, Y Mansour
UAI, 541-548, 2000
2912000
An experimental study of the coloring problem on human subject networks
M Kearns, S Suri, N Montfort
science 313 (5788), 824-827, 2006
2822006
Cryptographic primitives based on hard learning problems
A Blum, M Furst, M Kearns, RJ Lipton
Annual International Cryptology Conference, 278-291, 1993
2811993
On the learnability of discrete distributions
M Kearns, Y Mansour, D Ron, R Rubinfeld, RE Schapire, L Sellie
Proceedings of the twenty-sixth annual ACM symposium on Theory of computing …, 1994
2781994
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