Csaba Szepesvari - Google Scholar

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Citations	16501	10452
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Rémi MunosDeepMindVerified email at inria.fr
Andras GyorgyDeepMindVerified email at ualberta.ca
Yasin Abbasi-YadkoriDeepMindVerified email at google.com
Kocsis LeventeMTA SZTAKIVerified email at sztaki.hu
Lorincz, AndrasEotvos Lorand UniversityVerified email at inf.elte.hu
Tor LattimoreDeepMindVerified email at google.com
Richard S. SuttonDeepMind, Amii, and University of AlbertaVerified email at richsutton.com
Branislav KvetonGoogle ResearchVerified email at google.com
Amir-massoud FarahmandVector Institute, University of TorontoVerified email at vectorinstitute.ai
András AntosBudapest University of Technology and EconomicsVerified email at cs.bme.hu
David PalPrincipal Applied Researcher, Expedia GroupVerified email at expedia.com
Shalabh BhatnagarProfessor in the Department of Computer Science and Automation, Indian Institute of ScienceVerified email at iisc.ac.in
Mohammad GhavamzadehGoogle ResearchVerified email at google.com
Zheng WenDeepMindVerified email at google.com
Hamid MaeiCruise AutomationVerified email at getcruise.com
Michael LittmanBrown UniversityVerified email at brown.edu
Shie MannorProfessor of Electrical Engineering, TechnionVerified email at technion.ac.il
Gergely NeuArtificial Intelligence and Machine Learning group, Universitat Pompeu FabraVerified email at upf.edu
Gábor BartókETH ZurichVerified email at ualberta.ca
Dale SchuurmansResearch Scientist, Google Brain / Professor, University of AlbertaVerified email at cs.ualberta.ca

Csaba Szepesvari

DeepMind & Department of Computing Science, University of Alberta

Verified email at cs.ualberta.ca - Homepage

machine learning learning theory online learning reinforcement learning Markov Decision Processes


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Bandit based monte-carlo planning L Kocsis, C Szepesvári European conference on machine learning, 282-293, 2006	2796	2006
Algorithms for Reinforcement Learning C Szepesvari Morgan and Claypool, 2010	981	2010
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	711	2000
Improved algorithms for linear stochastic bandits Y Abbasi-Yadkori, C Szepesvári, D Pál Advances in Neural Information Processing Systems, 2312-2320, 2011	633	2011
Online optimization in X-armed bandits S Bubeck, G Stoltz, C Szepesvári, R Munos Advances in Neural Information Processing Systems, 201-208, 2009	497	2009
Exploration–exploitation tradeoff using variance estimates in multi-armed bandits JY Audibert, R Munos, C Szepesvári Theoretical Computer Science 410 (19), 1876-1902, 2009	487	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	469	2009
Bandit algorithms T Lattimore, C Szepesvári Cambridge University Press, 2020	323	2020
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	292	2008
Finite-time bounds for fitted value iteration R Munos, C Szepesvári Journal of Machine Learning Research 9 (May), 815-857, 2008	280	2008
A generalized reinforcement-learning model: Convergence and applications ML Littman, C Szepesvári ICML 96, 310-318, 1996	249	1996
Parametric bandits: The generalized linear case S Filippi, O Cappe, A Garivier, C Szepesvári Advances in Neural Information Processing Systems, 586-594, 2010	244	2010
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	234	2012
Toward off-policy learning control with function approximation HR Maei, C Szepesvári, S Bhatnagar, RS Sutton ICML, 2010	227	2010
Apprenticeship learning using inverse reinforcement learning and gradient methods G Neu, C Szepesvári arXiv preprint arXiv:1206.5264, 2012	225	2012
Multi-criteria reinforcement learning. Z Gábor, Z Kalmár, C Szepesvári ICML 98, 197-205, 1998	224	1998
Convergent temporal-difference learning with arbitrary smooth function approximation S Bhatnagar, D Precup, D Silver, RS Sutton, HR Maei, C Szepesvári Advances in Neural Information Processing Systems, 1204-1212, 2009	213	2009
Empirical bernstein stopping V Mnih, C Szepesvári, JY Audibert Proceedings of the 25th international conference on Machine learning, 672-679, 2008	204	2008
Learning with a strong adversary R Huang, B Xu, D Schuurmans, C Szepesvári arXiv preprint arXiv:1511.03034, 2015	184	2015
Fitted Q-iteration in continuous action-space MDPs A Antos, C Szepesvári, R Munos Advances in neural information processing systems, 9-16, 2008	184	2008

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