Theo dõi
Michael Kearns
Tiêu đề
Trích dẫn bởi
Trích dẫn bởi
Năm
An introduction to computational learning theory
MJ Kearns, U Vazirani
MIT press, 1994
23411994
Cryptographic limitations on learning boolean formulae and finite automata
M Kearns, L Valiant
Journal of the ACM (JACM) 41 (1), 67-95, 1994
13791994
Near-optimal reinforcement learning in polynomial time
M Kearns, S Singh
Machine learning 49, 209-232, 2002
13152002
Efficient noise-tolerant learning from statistical queries
M Kearns
Journal of the ACM (JACM) 45 (6), 983-1006, 1998
11021998
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
10482021
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
8192018
Graphical models for game theory
M Kearns, ML Littman, S Singh
arXiv preprint arXiv:1301.2281, 2013
8112013
A sparse sampling algorithm for near-optimal planning in large Markov decision processes
M Kearns, Y Mansour, AY Ng
Machine learning 49, 193-208, 2002
7782002
Toward efficient agnostic learning
MJ Kearns, RE Schapire, LM Sellie
Proceedings of the fifth annual workshop on Computational learning theory …, 1992
6971992
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
6801997
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
6461989
Learning in the presence of malicious errors
M Kearns, M Li
Proceedings of the twentieth annual ACM symposium on Theory of computing …, 1988
6411988
Fairness in learning: Classic and contextual bandits
M Joseph, M Kearns, JH Morgenstern, A Roth
Advances in neural information processing systems 29, 2016
5112016
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
5062002
The ethical algorithm: The science of socially aware algorithm design
M Kearns, A Roth
Oxford University Press, 2019
4802019
On the complexity of teaching
SA Goldman, MJ Kearns
Journal of Computer and System Sciences 50 (1), 20-31, 1995
4391995
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
4031987
Cryptographic primitives based on hard learning problems
A Blum, M Furst, M Kearns, RJ Lipton
Annual International Cryptology Conference, 278-291, 1993
4021993
Nash Convergence of Gradient Dynamics in General-Sum Games.
S Singh, MJ Kearns, Y Mansour
UAI, 541-548, 2000
3782000
The computational complexity of machine learning
MJ Kearns
MIT press, 1990
3781990
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