Michael Kearns
Zitiert von
Zitiert von
An introduction to computational learning theory
MJ Kearns, UV Vazirani, U Vazirani
MIT press, 1994
Cryptographic limitations on learning Boolean formulae and finite automata
M Kearns, L Valiant
Journal of the ACM (JACM) 41 (1), 67-95, 1994
Near-optimal reinforcement learning in polynomial time
M Kearns, S Singh
Machine learning 49 (2-3), 209-232, 2002
Efficient noise-tolerant learning from statistical queries
M Kearns
Journal of the ACM (JACM) 45 (6), 983-1006, 1998
Graphical models for game theory
M Kearns, ML Littman, S Singh
arXiv preprint arXiv:1301.2281, 2013
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
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
Toward efficient agnostic learning
MJ Kearns, RE Schapire, LM Sellie
Machine Learning 17 (2-3), 115-141, 1994
Learning in the presence of malicious errors
M Kearns, M Li
SIAM Journal on Computing 22 (4), 807-837, 1993
Algorithmic stability and sanity-check bounds for leave-one-out cross-validation
M Kearns, D Ron
Neural computation 11 (6), 1427-1453, 1999
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
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
On the complexity of teaching
SA Goldman, MJ Kearns
Journal of Computer and System Sciences 50 (1), 20-31, 1995
Modeling the IT value paradox
ME Thatcher, DE Pingry
Communications of the ACM 50 (8), 41-45, 2007
Nash Convergence of Gradient Dynamics in General-Sum Games.
SP Singh, MJ Kearns, Y Mansour
UAI, 541-548, 2000
Fairness in criminal justice risk assessments: The state of the art
R Berk, H Heidari, S Jabbari, M Kearns, A Roth
Sociological Methods & Research, 0049124118782533, 2018
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
Cryptographic primitives based on hard learning problems
A Blum, M Furst, M Kearns, RJ Lipton
Annual International Cryptology Conference, 278-291, 1993
An experimental study of the coloring problem on human subject networks
M Kearns, S Suri, N Montfort
science 313 (5788), 824-827, 2006
On the boosting ability of top–down decision tree learning algorithms
M Kearns, Y Mansour
Journal of Computer and System Sciences 58 (1), 109-128, 1999
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