Csaba Szepesvari
Csaba Szepesvari
DeepMind & Department of Computing Science, University of Alberta
Adresse e-mail validée de cs.ualberta.ca - Page d'accueil
Citée par
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Bandit based monte-carlo planning
L Kocsis, C Szepesvári
European conference on machine learning, 282-293, 2006
Algorithms for Reinforcement Learning
C Szepesvari
Morgan and Claypool, 2010
Improved algorithms for linear stochastic bandits
Y Abbasi-Yadkori, C Szepesvári, D Pál
Advances in Neural Information Processing Systems, 2312-2320, 2011
Convergence results for single-step on-policy reinforcement-learning algorithms
S Singh, T Jaakkola, ML Littman, C Szepesvári
Machine learning 38 (3), 287-308, 2000
Bandit algorithms
T Lattimore, C Szepesvári
Cambridge University Press, 2020
X-Armed Bandits.
S Bubeck, R Munos, G Stoltz, C Szepesvári
Journal of Machine Learning Research 12 (5), 2011
Exploration–exploitation tradeoff using variance estimates in multi-armed bandits
JY Audibert, R Munos, C Szepesvári
Theoretical Computer Science 410 (19), 1876-1902, 2009
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
Finite-Time Bounds for Fitted Value Iteration.
R Munos, C Szepesvári
Journal of Machine Learning Research 9 (5), 2008
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 (1), 89-129, 2008
Parametric Bandits: The Generalized Linear Case.
S Filippi, O Cappe, A Garivier, C Szepesvári
NIPS 23, 586-594, 2010
A generalized reinforcement-learning model: Convergence and applications
ML Littman, C Szepesvári
ICML 96, 310-318, 1996
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
Apprenticeship learning using inverse reinforcement learning and gradient methods
G Neu, C Szepesvári
arXiv preprint arXiv:1206.5264, 2012
Toward off-policy learning control with function approximation
HR Maei, C Szepesvári, S Bhatnagar, RS Sutton
ICML, 2010
Learning with a strong adversary
R Huang, B Xu, D Schuurmans, C Szepesvári
arXiv preprint arXiv:1511.03034, 2015
Convergent temporal-difference learning with arbitrary smooth function approximation.
HR Maei, C Szepesvari, S Bhatnagar, D Precup, D Silver, RS Sutton
NIPS, 1204-1212, 2009
Multi-criteria reinforcement learning.
Z Gábor, Z Kalmár, C Szepesvári
ICML 98, 197-205, 1998
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
Empirical bernstein stopping
V Mnih, C Szepesvári, JY Audibert
Proceedings of the 25th international conference on Machine learning, 672-679, 2008
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