| Bandit based monte-carlo planning L Kocsis, C Szepesvári European conference on machine learning, 282-293, 2006 | 3620 | 2006 |
| Algorithms for Reinforcement Learning C Szepesvari Morgan and Claypool, 2010 | 1586 | 2010 |
| Bandit algorithms T Lattimore, C Szepesvári Cambridge University Press, 2020 | 1557 | 2020 |
| Improved algorithms for linear stochastic bandits Y Abbasi-Yadkori, C Szepesvári, D Pál Advances in Neural Information Processing Systems, 2312-2320, 2011 | 1347 | 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 | 884 | 2000 |
| Online optimization in X-armed bandits S Bubeck, G Stoltz, C Szepesvári, R Munos Advances in Neural Information Processing Systems 21, 2008 | 646 | 2008 |
| Exploration–exploitation tradeoff using variance estimates in multi-armed bandits JY Audibert, R Munos, C Szepesvári Theoretical Computer Science 410 (19), 1876-1902, 2009 | 645 | 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 | 622 | 2009 |
| Finite-Time Bounds for Fitted Value Iteration. R Munos, C Szepesvári Journal of Machine Learning Research 9 (5), 2008 | 459 | 2008 |
| Parametric bandits: The generalized linear case S Filippi, O Cappe, A Garivier, C Szepesvári Advances in Neural Information Processing Systems 23, 2010 | 418 | 2010 |
| 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 | 412 | 2008 |
| Learning with a strong adversary R Huang, B Xu, D Schuurmans, C Szepesvári arXiv preprint arXiv:1511.03034, 2015 | 346 | 2015 |
| 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 | 326 | 2011 |
| A generalized reinforcement-learning model: Convergence and applications ML Littman, C Szepesvári ICML 96, 310-318, 1996 | 307 | 1996 |
| 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 | 293 | 2010 |
| 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 | 288 | 2009 |
| 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 | 287 | 2012 |
| Apprenticeship learning using inverse reinforcement learning and gradient methods G Neu, C Szepesvári arXiv preprint arXiv:1206.5264, 2012 | 286 | 2012 |
| Multi-criteria reinforcement learning. Z Gábor, Z Kalmár, C Szepesvári ICML 98, 197-205, 1998 | 275 | 1998 |
| 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 | 255 | 2015 |
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