| Bandit based monte-carlo planning L Kocsis, C Szepesvári European conference on machine learning, 282-293, 2006 | 4438 | 2006 |
| Bandit algorithms T Lattimore, C Szepesvári Cambridge University Press, 2020 | 3066 | 2020 |
| Algorithms for Reinforcement Learning C Szepesvari Morgan and Claypool, 2010 | 2177* | 2010 |
| Improved algorithms for linear stochastic bandits Y Abbasi-Yadkori, C Szepesvári, D Pál Advances in Neural Information Processing Systems, 2312-2320, 2011 | 2068 | 2011 |
| Convergence results for single-step on-policy reinforcement-learning algorithms S Singh, T Jaakkola, ML Littman, C Szepesvári Machine learning 38, 287-308, 2000 | 1032 | 2000 |
| Exploration–exploitation tradeoff using variance estimates in multi-armed bandits JY Audibert, R Munos, C Szepesvári Theoretical Computer Science 410 (19), 1876-1902, 2009 | 792 | 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 | 728 | 2009 |
| Finite-Time Bounds for Fitted Value Iteration. R Munos, C Szepesvári Journal of Machine Learning Research 9 (5), 2008 | 648 | 2008 |
| Parametric bandits: The generalized linear case S Filippi, O Cappe, A Garivier, C Szepesvári Advances in neural information processing systems 23, 2010 | 556 | 2010 |
| X-Armed Bandits. S Bubeck, R Munos, G Stoltz, C Szepesvári Journal of Machine Learning Research 12 (5), 2011 | 511 | 2011 |
| 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, 89-129, 2008 | 506 | 2008 |
| Learning with a strong adversary R Huang, B Xu, D Schuurmans, C Szepesvári arXiv preprint arXiv:1511.03034, 2015 | 454 | 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 | 443 | 2011 |
| 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 | 353 | 2009 |
| A generalized reinforcement-learning model: Convergence and applications ML Littman, C Szepesvári ICML 96, 310-318, 1996 | 353 | 1996 |
| Toward off-policy learning control with function approximation. HR Maei, C Szepesvári, S Bhatnagar, RS Sutton ICML 10, 719-726, 2010 | 343 | 2010 |
| Tight regret bounds for stochastic combinatorial semi-bandits B Kveton, Z Wen, A Ashkan, C Szepesvari Artificial Intelligence and Statistics, 535-543, 2015 | 334 | 2015 |
| Model-based reinforcement learning with value-targeted regression A Ayoub, Z Jia, C Szepesvari, M Wang, L Yang International Conference on Machine Learning, 463-474, 2020 | 331 | 2020 |
| Online learning under delayed feedback P Joulani, A Gyorgy, C Szepesvári International conference on machine learning, 1453-1461, 2013 | 328 | 2013 |
| 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 | 324 | 2012 |
Tor LattimoreDeepMind在 google.com 的电子邮件经过验证
