| A contextual-bandit approach to personalized news article recommendation L Li, W Chu, J Langford, RE Schapire Proceedings of the 19th international conference on World wide web, 661-670, 2010 | 1204 | 2010 |
| Parallelized stochastic gradient descent M Zinkevich, M Weimer, L Li, AJ Smola Advances in neural information processing systems, 2595-2603, 2010 | 810 | 2010 |
| An empirical evaluation of thompson sampling O Chapelle, L Li Advances in neural information processing systems, 2249-2257, 2011 | 692 | 2011 |
| Sparse online learning via truncated gradient J Langford, L Li, T Zhang Journal of Machine Learning Research 10 (Mar), 777-801, 2009 | 425 | 2009 |
| Contextual bandits with linear payoff functions W Chu, L Li, L Reyzin, R Schapire Proceedings of the Fourteenth International Conference on Artificial …, 2011 | 354 | 2011 |
| Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms L Li, W Chu, J Langford, X Wang Proceedings of the fourth ACM international conference on Web search and …, 2011 | 321 | 2011 |
| PAC model-free reinforcement learning AL Strehl, L Li, E Wiewiora, J Langford, ML Littman Proceedings of the 23rd international conference on Machine learning, 881-888, 2006 | 311 | 2006 |
| Towards a Unified Theory of State Abstraction for MDPs. L Li, TJ Walsh, ML Littman ISAIM, 2006 | 241 | 2006 |
| Doubly robust policy evaluation and learning M Dudík, J Langford, L Li arXiv preprint arXiv:1103.4601, 2011 | 217 | 2011 |
| Doubly Robust Policy Evaluation and Learning M Dudık, J Langford, L Li | 217* | |
| Taming the monster: A fast and simple algorithm for contextual bandits A Agarwal, D Hsu, S Kale, J Langford, L Li, R Schapire International Conference on Machine Learning, 1638-1646, 2014 | 209 | 2014 |
| Knows what it knows: a framework for self-aware learning L Li, ML Littman, TJ Walsh Proceedings of the 25th international conference on Machine learning, 568-575, 2008 | 201 | 2008 |
| Reinforcement learning in finite MDPs: PAC analysis AL Strehl, L Li, ML Littman Journal of Machine Learning Research 10 (Nov), 2413-2444, 2009 | 197 | 2009 |
| A Bayesian sampling approach to exploration in reinforcement learning J Asmuth, L Li, ML Littman, A Nouri, D Wingate Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial …, 2009 | 152 | 2009 |
| An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning R Parr, L Li, G Taylor, C Painter-Wakefield, ML Littman Proceedings of the 25th international conference on Machine learning, 752-759, 2008 | 152 | 2008 |
| Analyzing feature generation for value-function approximation R Parr, C Painter-Wakefield, L Li, M Littman Proceedings of the 24th international conference on Machine learning, 737-744, 2007 | 147 | 2007 |
| Contextual bandit algorithms with supervised learning guarantees A Beygelzimer, J Langford, L Li, L Reyzin, RE Schapire Arxiv preprint arXiv:1002.4058, 2010 | 139 | 2010 |
| Doubly robust off-policy value evaluation for reinforcement learning N Jiang, L Li arXiv preprint arXiv:1511.03722, 2015 | 121 | 2015 |
| Learning from logged implicit exploration data A Strehl, J Langford, L Li, SM Kakade Advances in Neural Information Processing Systems, 2217-2225, 2010 | 119 | 2010 |
| End-to-end task-completion neural dialogue systems X Li, YN Chen, L Li, J Gao, A Celikyilmaz arXiv preprint arXiv:1703.01008, 2017 | 117 | 2017 |
John LangfordMicrosoft Research New YorkVerified email at hunch.net