Obserwuj
Csaba Szepesvari
Tytuł
Cytowane przez
Cytowane przez
Rok
Bandit based monte-carlo planning
L Kocsis, C Szepesvári
European conference on machine learning, 282-293, 2006
40132006
Bandit algorithms
T Lattimore, C Szepesvári
Cambridge University Press, 2020
22862020
Algorithms for Reinforcement Learning
C Szepesvari
Morgan and Claypool, 2010
1960*2010
Improved algorithms for linear stochastic bandits
Y Abbasi-Yadkori, C Szepesvári, D Pál
Advances in Neural Information Processing Systems, 2312-2320, 2011
17202011
Convergence results for single-step on-policy reinforcement-learning algorithms
S Singh, T Jaakkola, ML Littman, C Szepesvári
Machine learning 38, 287-308, 2000
9502000
Exploration–exploitation tradeoff using variance estimates in multi-armed bandits
JY Audibert, R Munos, C Szepesvári
Theoretical Computer Science 410 (19), 1876-1902, 2009
7352009
Fast gradient-descent methods for temporal-difference learning with linear function approximation
RS Sutton, HR Maei, D Precup, S Bhatnagar, D Silver, C Szepesvári, ...
Proceedings of the 26th annual international conference on machine learning …, 2009
6732009
Finite-Time Bounds for Fitted Value Iteration.
R Munos, C Szepesvári
Journal of Machine Learning Research 9 (5), 2008
5752008
Parametric bandits: The generalized linear case
S Filippi, O Cappe, A Garivier, C Szepesvári
Advances in neural information processing systems 23, 2010
5012010
X-Armed Bandits.
S Bubeck, R Munos, G Stoltz, C Szepesvári
Journal of Machine Learning Research 12 (5), 2011
4852011
Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path
A Antos, C Szepesvári, R Munos
Machine Learning 71, 89-129, 2008
4692008
Learning with a strong adversary
R Huang, B Xu, D Schuurmans, C Szepesvári
arXiv preprint arXiv:1511.03034, 2015
4142015
Regret bounds for the adaptive control of linear quadratic systems
Y Abbasi-Yadkori, C Szepesvári
Proceedings of the 24th Annual Conference on Learning Theory, 1-26, 2011
3912011
A generalized reinforcement-learning model: Convergence and applications
ML Littman, C Szepesvári
ICML 96, 310-318, 1996
3291996
Toward off-policy learning control with function approximation.
HR Maei, C Szepesvári, S Bhatnagar, RS Sutton
ICML 10, 719-726, 2010
3212010
Apprenticeship learning using inverse reinforcement learning and gradient methods
G Neu, C Szepesvári
arXiv preprint arXiv:1206.5264, 2012
3152012
Convergent temporal-difference learning with arbitrary smooth function approximation
H Maei, C Szepesvari, S Bhatnagar, D Precup, D Silver, RS Sutton
Advances in neural information processing systems 22, 2009
3142009
The grand challenge of computer Go: Monte Carlo tree search and extensions
S Gelly, L Kocsis, M Schoenauer, M Sebag, D Silver, C Szepesvári, ...
Communications of the ACM 55 (3), 106-113, 2012
3092012
Cascading bandits: Learning to rank in the cascade model
B Kveton, C Szepesvari, Z Wen, A Ashkan
International conference on machine learning, 767-776, 2015
2982015
Multi-criteria reinforcement learning.
Z Gábor, Z Kalmár, C Szepesvári
ICML 98, 197-205, 1998
2951998
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