Lihong Li (李力鸿)
Lihong Li (李力鸿)
Research Scientist, Google
Verified email at - Homepage
Cited by
Cited by
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
Parallelized stochastic gradient descent
M Zinkevich, M Weimer, L Li, AJ Smola
Advances in neural information processing systems, 2595-2603, 2010
An empirical evaluation of thompson sampling
O Chapelle, L Li
Advances in neural information processing systems, 2249-2257, 2011
Sparse online learning via truncated gradient
J Langford, L Li, T Zhang
Journal of Machine Learning Research 10 (Mar), 777-801, 2009
Contextual bandits with linear payoff functions
W Chu, L Li, L Reyzin, R Schapire
Proceedings of the Fourteenth International Conference on Artificial …, 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
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
Doubly robust policy evaluation and learning
M Dudík, J Langford, L Li
arXiv preprint arXiv:1103.4601, 2011
Doubly Robust Policy Evaluation and Learning
M Dudık, J Langford, L Li
Towards a Unified Theory of State Abstraction for MDPs.
L Li, TJ Walsh, ML Littman
ISAIM, 2006
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
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
Reinforcement learning in finite MDPs: PAC analysis
AL Strehl, L Li, ML Littman
Journal of Machine Learning Research 10 (Nov), 2413-2444, 2009
Doubly robust off-policy value evaluation for reinforcement learning
N Jiang, L Li
arXiv preprint arXiv:1511.03722, 2015
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
Towards end-to-end reinforcement learning of dialogue agents for information access
B Dhingra, L Li, X Li, J Gao, YN Chen, F Ahmed, L Deng
arXiv preprint arXiv:1609.00777, 2016
Contextual bandit algorithms with supervised learning guarantees
A Beygelzimer, J Langford, L Li, L Reyzin, RE Schapire
Arxiv preprint arXiv:1002.4058, 2010
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
End-to-end task-completion neural dialogue systems
X Li, YN Chen, L Li, J Gao, A Celikyilmaz
arXiv preprint arXiv:1703.01008, 2017
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
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