Csaba Szepesvári, a Hungarian computer scientiest with research interests in applications of statistical techniques in AI, and Reinforcement Learning [1]. Csaba Szepesvári worked at the Computer and Automation Research Institute of the Hungarian Academy of Sciences, and is actually Associate Professor [2] at the Department of Computing Science, University of Alberta and is principal investigator of the RLAI [3] group. In 2006, together with Levente Kocsis, Csaba Szepesvári introduced UCT (Upper Confidence bounds applied to Trees), a new algorithm that applies bandit ideas to guide Monte-Carlo planning [4].
Csaba Szepesvári [5]

Selected Publications

1994 ...

• Csaba Szepesvári, Lászlo Balázs, András Lõrincz (1994). Topology learning solved by extended objects: a neural network model. pdf
• Csaba Szepesvári (1998). Reinforcement Learning: Theory and Practice. in Proceedings of the 2nd Slovak Conference on Artificial Neural Networks, zipped ps

2005 ...

• Levente Kocsis, Csaba Szepesvári, Mark Winands (2005). RSPSA: Enhanced Parameter Optimization in Games. Advances in Computer Games 11, pdf
• Levente Kocsis, Csaba Szepesvári (2006). Universal Parameter Optimisation in Games Based on SPSA. Machine Learning, Special Issue on Machine Learning and Games, Vol. 63, No. 3
• Levente Kocsis, Csaba Szepesvári (2006). Bandit based Monte-Carlo Planning. ECML-06, LNCS/LNAI 4212, pp. 282-293. introducing UCT, pdf
• Levente Kocsis, Csaba Szepesvári, Jan Willemson (2006). Improved Monte-Carlo Search. pdf
• András György, Levente Kocsis, Ivett Szabó, Csaba Szepesvári (2007). Continuous Time Associative Bandit Problems IJCAI-07, 830-835. pdf
• Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári (2007). Tuning Bandit Algorithms in Stochastic Environments. pdf
• Richard Sutton, Csaba Szepesvári, Hamid Reza Maei (2008). A Convergent O(n) Algorithm for Off-policy Temporal-difference Learning with Linear Function Approximation, pdf (draft)
• Rémi Munos, Csaba Szepesvári (2008). Finite time bounds for sampling based fitted value iteration. Journal of Machine Learning Research, 9:815-857, 2008. pdf, pdf
• Hamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, Doina Precup, David Silver, Richard Sutton (2009). Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. Accepted in Advances in Neural Information Processing Systems 22, Vancouver, BC. December 2009. MIT Press. pdf
• Richard Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora. (2009). Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation. In Proceedings of the 26th International Conference on Machine Learning (ICML-09). pdf
• Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári (2009). Exploration-exploitation trade-off using variance estimates in multi-armed bandits. Theoretical Computer Science, 410:1876-1902, 2009, pdf

2010 ...

• István Szita, Csaba Szepesvári (2010). Model-based reinforcement learning with nearly tight exploration complexity bounds. ICML 2010
• István Szita, Csaba Szepesvári (2011). Agnostic KWIK learning and efficient approximate reinforcement learning. Journal of Machine Learning Research - Proceedings Track 19
• Sylvain Gelly, Marc Schoenauer, Michèle Sebag, Olivier Teytaud, Levente Kocsis, David Silver, Csaba Szepesvári (2012). The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions. Communications of the ACM, Vol. 55, No. 3, pdf preprint

External Links

• Homepage of Csaba Szepesvári from University of Alberta
• Csaba, Szepesvári, PhD. Senior Research Scientist from Hungarian Academy of Sciences
• Introduction to Reinforcement Learning, videolecture by Csaba Szepesvári, 2008

References

1. ^ Research Interests of Csaba Szepesvári
2. ^ The Alberta Ingenuity Fund
3. ^ Reinforcement Learning and Artificial Intelligence (RLAI)
4. ^ Levente Kocsis, Csaba Szepesvári (2006). Bandit based Monte-Carlo Planning
5. ^ Homepage of Csaba Szepesvári
6. ^ On-line publications of Csaba Szepesvári
7. ^ dblp: Csaba Szepesvári

What links here?