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Wei Chen

Wei Chen (陈薇) is an associate researcher in Internet Economics and Computational Advertising Group, Microsoft Research Asia. Her current research interests include: machine learning for games, deep learning theory, mechanism design, and learning to rank. Before she joined Microsoft in July 2011, she obtained her Ph. D. in probability and mathematic statistics from Academy of Mathematics and System Science, Chinese Academy of Sciences.



Tie-Yan Liu, Wei Chen, and Tao Qin, Mechanism Learning with Mechanism Induced Data, Senior Member Track, AAAI 2015.

Haifang Li, Tian Fei, Wei Chen, Tao Qin, Zhiming Ma, and Tie-Yan Liu, Generalization Analysis for Game-Theoretic Machine Learning, AAAI 2015.

Tao Qin, Wei Chen, and Tie-Yan Liu, Sponsored Search Auctions: Recent Advances and Future Directions, ACM Transactions on Intelligent Systems and Technology, 2014.

Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, and Liwei Wang, Generalized Second Price Auction with Probabilistic Broad Match, EC 2014.

Fei Tian, Haifang Li, Wei Chen, Tao Qin and Tie-Yan Liu, Agent Behavior Prediction and Its Generalization Analysis, AAAI 2014.

Jun Feng, Jiang Bian, Taifeng Wang, Wei Chen, Xiaoyan Zhu and Tie-Yan Liu, Sampling Dilemma: Towards Effective Data Sampling for Click Prediction in Sponsored Search, WSDM 2014.

Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu, Online Learning for Auction Mechanism in Bandit Setting, Decision Surpport Systems, 2013.

Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu, Probabilistic Broad Match for Sponsored Search Auctions, EC AdAuction Workshop 2013.

Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen, and Tie-Yan Liu, A Theoretical Analysis of NDCG Type Ranking Measures, COLT 2013.

Di He, Wei Chen, Liwei Wang, Tie-Yan Liu, A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search, IJCAI 2013.

Lei Yao, Wei Chen, Tie-Yan Liu. Convergence Analysis for Weighted Joint Strategy Fictitious Play in Generalized Second Price Auction, WINE 2012.

Wei Chen, Tie-Yan Liu, and Zhiming Ma, Two-Layer Generalization Analysis for Ranking Using Rademacher Average, NIPS 2010.

Wei Chen, Tie-Yan Liu, Yanyan Lan, and Zhiming Ma, Ranking Measures and Loss functions in Learning to Rank, NIPS 2009.



Mailing Address: Tower 2, No. 5 Danling Street, Haidian District, Beijing, P. R. China, 100080