Difference between revisions of "Csaba Szepesvári"

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* [[István Szita]], [[Csaba Szepesvári]] ('''2011'''). ''Agnostic KWIK learning and efficient approximate reinforcement learning''. [http://www.informatik.uni-trier.de/~ley/db/journals/jmlr/jmlrp19.html#SzitaS11 Journal of Machine Learning Research - Proceedings Track 19]
 
* [[István Szita]], [[Csaba Szepesvári]] ('''2011'''). ''Agnostic KWIK learning and efficient approximate reinforcement learning''. [http://www.informatik.uni-trier.de/~ley/db/journals/jmlr/jmlrp19.html#SzitaS11 Journal of Machine Learning Research - Proceedings Track 19]
 
* [[Sylvain Gelly]], [[Marc Schoenauer]], [[Michèle Sebag]], [[Olivier Teytaud]], [[Levente Kocsis]], [[David Silver]], [[Csaba Szepesvári]] ('''2012'''). ''[http://dl.acm.org/citation.cfm?id=2093548.2093574 The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions]''. [[ACM#Communications|Communications of the ACM]], Vol. 55, No. 3, [http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Applications_files/grand-challenge.pdf pdf preprint]
 
* [[Sylvain Gelly]], [[Marc Schoenauer]], [[Michèle Sebag]], [[Olivier Teytaud]], [[Levente Kocsis]], [[David Silver]], [[Csaba Szepesvári]] ('''2012'''). ''[http://dl.acm.org/citation.cfm?id=2093548.2093574 The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions]''. [[ACM#Communications|Communications of the ACM]], Vol. 55, No. 3, [http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Applications_files/grand-challenge.pdf pdf preprint]
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* [https://scholar.google.com/citations?user=iuCvTuIAAAAJ&hl=en Mahdi Milani Fard], [[Joelle Pineau]], [[Csaba Szepesvári]] ('''2012'''). ''PAC-Bayesian Policy Evaluation for Reinforcement Learning''. [https://arxiv.org/abs/1202.3717 arXiv:1202.3717]
 
==2015 ...==
 
==2015 ...==
 
* [[Tor Lattimore]], [[Csaba Szepesvári]] ('''2017'''). ''The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits''.  [https://www.aistats.org/aistats2017/ AISTATS], [https://sites.ualberta.ca/~szepesva/papers/linbandits_aistats17.pdf pdf]
 
* [[Tor Lattimore]], [[Csaba Szepesvári]] ('''2017'''). ''The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits''.  [https://www.aistats.org/aistats2017/ AISTATS], [https://sites.ualberta.ca/~szepesva/papers/linbandits_aistats17.pdf pdf]

Latest revision as of 22:16, 12 April 2021

Home * People * Csaba Szepesvári

Csaba Szepesvári [1]

Csaba Szepesvári,
a Hungarian computer scientiest with research interests in applications of statistical techniques in AI, and Reinforcement Learning [2]. Csaba Szepesvári worked at the Computer and Automation Research Institute of the Hungarian Academy of Sciences, and is professor at the Department of Computing Science, University of Alberta, and principal investigator of the RLAI [3] group, actually on leave at DeepMind.

UCT

In 2006, along 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]. UCT accelerated the Monte-Carlo revolution in computer Go [5] and other domains.

Selected Publications

[6] [7]

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External Links

References

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