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Csaba Szepesvari
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Bandit based monte-carlo planning
L Kocsis, C Szepesvári
European conference on machine learning, 282-293, 2006
46622006
Bandit algorithms
T Lattimore, C Szepesvári
Cambridge University Press, 2020
34092020
Algorithms for Reinforcement Learning
C Szepesvari
Morgan and Claypool, 2010
2284*2010
Improved algorithms for linear stochastic bandits
Y Abbasi-Yadkori, C Szepesvári, D Pál
Advances in Neural Information Processing Systems, 2312-2320, 2011
22212011
Convergence results for single-step on-policy reinforcement-learning algorithms
S Singh, T Jaakkola, ML Littman, C Szepesvári
Machine learning 38, 287-308, 2000
10652000
Exploration–exploitation tradeoff using variance estimates in multi-armed bandits
JY Audibert, R Munos, C Szepesvári
Theoretical Computer Science 410 (19), 1876-1902, 2009
8212009
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
7522009
Finite-Time Bounds for Fitted Value Iteration.
R Munos, C Szepesvári
Journal of Machine Learning Research 9 (5), 2008
6942008
Parametric bandits: The generalized linear case
S Filippi, O Cappe, A Garivier, C Szepesvári
Advances in neural information processing systems 23, 2010
5922010
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
5342008
X-Armed Bandits.
S Bubeck, R Munos, G Stoltz, C Szepesvári
Journal of Machine Learning Research 12 (5), 2011
5332011
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
4642011
Learning with a strong adversary
R Huang, B Xu, D Schuurmans, C Szepesvári
arXiv preprint arXiv:1511.03034, 2015
4572015
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
3682009
A generalized reinforcement-learning model: Convergence and applications
ML Littman, C Szepesvári
ICML 96, 310-318, 1996
3651996
Tight regret bounds for stochastic combinatorial semi-bandits
B Kveton, Z Wen, A Ashkan, C Szepesvari
Artificial Intelligence and Statistics, 535-543, 2015
3572015
Toward off-policy learning control with function approximation.
HR Maei, C Szepesvári, S Bhatnagar, RS Sutton
ICML 10, 719-726, 2010
3562010
Model-based reinforcement learning with value-targeted regression
A Ayoub, Z Jia, C Szepesvari, M Wang, L Yang
International Conference on Machine Learning, 463-474, 2020
3492020
Online learning under delayed feedback
P Joulani, A Gyorgy, C Szepesvári
International conference on machine learning, 1453-1461, 2013
3492013
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
3362015
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