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Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision

Feb 24, 2020
Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu


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Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation

Oct 16, 2019
Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu


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Neural Phrase-to-Phrase Machine Translation

Nov 06, 2018
Jiangtao Feng, Lingpeng Kong, Po-Sen Huang, Chong Wang, Da Huang, Jiayuan Mao, Kan Qiao, Dengyong Zhou


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Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation

Oct 29, 2018
Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou

* 21 pages, 5 figures, NIPS 2018 (spotlight) 

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Towards Neural Phrase-based Machine Translation

Sep 24, 2018
Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng

* in International Conference on Learning Representations (ICLR) 2018 

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Sequence Modeling via Segmentations

Jul 18, 2018
Chong Wang, Yining Wang, Po-Sen Huang, Abdelrahman Mohamed, Dengyong Zhou, Li Deng

* recurrent neural networks, dynamic programming, structured prediction 

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On the Discrimination-Generalization Tradeoff in GANs

Feb 23, 2018
Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He

* ICLR 2018 

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Action-depedent Control Variates for Policy Optimization via Stein's Identity

Feb 23, 2018
Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu

* The first two authors contributed equally. Author ordering determined by coin flip over a Google Hangout. Accepted by ICLR 2018 

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Provably Optimal Algorithms for Generalized Linear Contextual Bandits

Jun 18, 2017
Lihong Li, Yu Lu, Dengyong Zhou

* Published at ICML 2017 

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Stochastic Variance Reduction Methods for Policy Evaluation

Jun 09, 2017
Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou

* Accepted by ICML 2017 

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Neuro-Symbolic Program Synthesis

Nov 06, 2016
Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, Pushmeet Kohli


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Minimax Optimal Convergence Rates for Estimating Ground Truth from Crowdsourced Labels

May 30, 2016
Chao Gao, Dengyong Zhou


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Exact Exponent in Optimal Rates for Crowdsourcing

May 26, 2016
Chao Gao, Yu Lu, Dengyong Zhou

* To appear in the Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA, 2016 

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Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing

Dec 16, 2015
Nihar B. Shah, Dengyong Zhou


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Approval Voting and Incentives in Crowdsourcing

Sep 07, 2015
Nihar B. Shah, Dengyong Zhou, Yuval Peres


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Regularized Minimax Conditional Entropy for Crowdsourcing

Mar 25, 2015
Dengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek, Nihar B. Shah

* 31 pages 

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On the Impossibility of Convex Inference in Human Computation

Nov 21, 2014
Nihar B. Shah, Dengyong Zhou

* AAAI 2015 

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Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing

Nov 01, 2014
Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan


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Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling

Apr 24, 2014
Xi Chen, Qihang Lin, Dengyong Zhou

* 39 pages 

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Error Rate Bounds in Crowdsourcing Models

Jul 10, 2013
Hongwei Li, Bin Yu, Dengyong Zhou

* 13 pages, 3 figures, downloadable supplementary files 

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