Παρακολούθηση
Lihong Li (李力鸿)
Lihong Li (李力鸿)
Amazon
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα amazon.com - Αρχική σελίδα
Τίτλος
Παρατίθεται από
Παρατίθεται από
Έτος
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
36392010
An empirical evaluation of thompson sampling
O Chapelle, L Li
Advances in neural information processing systems 24, 2011
19452011
Parallelized stochastic gradient descent
M Zinkevich, M Weimer, L Li, A Smola
Advances in neural information processing systems 23, 2010
18492010
Contextual bandits with linear payoff functions
W Chu, L Li, L Reyzin, R Schapire
Proceedings of the Fourteenth International Conference on Artificial …, 2011
13492011
Neural approaches to conversational AI
J Gao, M Galley, L Li
The 41st international ACM SIGIR conference on research & development in …, 2018
10092018
Doubly robust policy evaluation and learning
M Dudík, J Langford, L Li
arXiv preprint arXiv:1103.4601, 2011
9302011
Doubly Robust Policy Evaluation and Learning
M Dudık, J Langford, L Li
930*
Doubly robust off-policy value evaluation for reinforcement learning
N Jiang, L Li
International conference on machine learning, 652-661, 2016
8802016
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
7042011
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
6622006
Towards a unified theory of state abstraction for MDPs.
L Li, TJ Walsh, ML Littman
AI&M 1 (2), 3, 2006
6552006
Sparse Online Learning via Truncated Gradient.
J Langford, L Li, T Zhang
Journal of Machine Learning Research 10 (3), 2009
6112009
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
5982014
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
552*2016
Doubly robust policy evaluation and optimization
M Dudík, D Erhan, J Langford, L Li
5082014
End-to-end task-completion neural dialogue systems
X Li, YN Chen, L Li, J Gao, A Celikyilmaz
arXiv preprint arXiv:1703.01008, 2017
4712017
Neuro-symbolic program synthesis
E Parisotto, A Mohamed, R Singh, L Li, D Zhou, P Kohli
arXiv preprint arXiv:1611.01855, 2016
4312016
Breaking the curse of horizon: Infinite-horizon off-policy estimation
Q Liu, L Li, Z Tang, D Zhou
Advances in neural information processing systems 31, 2018
4062018
Reinforcement Learning in Finite MDPs: PAC Analysis.
AL Strehl, L Li, ML Littman
Journal of Machine Learning Research 10 (11), 2009
3962009
Contextual bandit algorithms with supervised learning guarantees
A Beygelzimer, J Langford, L Li, L Reyzin, RE Schapire
Arxiv preprint arXiv:1002.4058, 2010
3852010
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