Csaba Szepesvari

Cited by

	All	Since 2016
Citations	18084	10727
h-index	55	49
i10-index	181	140

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Co-authors

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

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


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Bandit based monte-carlo planning L Kocsis, C Szepesvári European conference on machine learning, 282-293, 2006	2947	2006
Algorithms for Reinforcement Learning C Szepesvari Morgan and Claypool, 2010	1138	2010
Improved algorithms for linear stochastic bandits Y Abbasi-Yadkori, C Szepesvári, D Pál Advances in Neural Information Processing Systems, 2312-2320, 2011	755	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	744	2000
X-Armed Bandits. S Bubeck, R Munos, G Stoltz, C Szepesvári Journal of Machine Learning Research 12 (5), 2011	542*	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	520	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	491	2009
Bandit algorithms T Lattimore, C Szepesvári Cambridge University Press, 2020	475	2020
Finite-Time Bounds for Fitted Value Iteration. R Munos, C Szepesvári Journal of Machine Learning Research 9 (5), 2008	308	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	308	2008
Parametric Bandits: The Generalized Linear Case. S Filippi, O Cappe, A Garivier, C Szepesvári NIPS 23, 586-594, 2010	272	2010
A generalized reinforcement-learning model: Convergence and applications ML Littman, C Szepesvári ICML 96, 310-318, 1996	259	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	248	2012
Apprenticeship learning using inverse reinforcement learning and gradient methods G Neu, C Szepesvári arXiv preprint arXiv:1206.5264, 2012	239	2012
Toward off-policy learning control with function approximation HR Maei, C Szepesvári, S Bhatnagar, RS Sutton Proceedings of the 27th International Conference on Machine Learning (ICML …, 2010	237	2010
Multi-criteria reinforcement learning. Z Gábor, Z Kalmár, C Szepesvári ICML 98, 197-205, 1998	230	1998
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	223	2009
Empirical bernstein stopping V Mnih, C Szepesvári, JY Audibert Proceedings of the 25th international conference on Machine learning, 672-679, 2008	211	2008
Learning with a strong adversary R Huang, B Xu, D Schuurmans, C Szepesvári arXiv preprint arXiv:1511.03034, 2015	207	2015
Improved rates for the stochastic continuum-armed bandit problem P Auer, R Ortner, C Szepesvári International Conference on Computational Learning Theory, 454-468, 2007	198	2007

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