Portrait of Tao Qin

Tao Qin

Lead Researcher


Dr. Tao Qin (秦涛) is currently a Lead Researcher in Microsoft Research Asia. His research interests include machine learning (with the focus on deep learning and reinforcement learning), artificial intelligence (with applications to robotics), game theory (with applications to cloud computing, online and mobile advertising, ecommerce), information retrieval and computational advertising. He got his PhD degree and Bachelor degree both from Tsinghua University. He is a member of ACM and IEEE, and an Adjunct Professor (PhD advisor) in the University of Science and Technology of China.

Selected Publications
Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu and Tie-Yan Liu, Dual Supervised LearningICML-2017.
Lijun Wu, Li Zhao, Tao Qin, and Tie-Yan Liu, Sequence Prediction with Unlabeled Data by Reward Function Learning, IJCAI 2017.
Yingce Xia, Jiang Bian, Tao Qin, Tie-Yan Liu, Dual Inference for Machine Learning, IJCAI 2017.
Yingce Xia, Tao Qin, Wenkui Ding, Haifang Li, Xu-Dong Zhang, Nenghai Yu and Tie-Yan Liu, Finite Budget Analysis of Multi-armed Bandit Problems, Neurocomputing.
Chang Xu, Tao Qin, Yalong Bai, Gang Wang and Tie-Yan Liu, Convolutional Neural Networks for Posed and Spontaneous Expression Recognition, ICME 2017.
Jiang Rong, Tao Qin, Bo An and Tie-Yan Liu, Pricing Optimization for Selling Reusable Resources, AAMAS 2017.
Jiang Rong, Tao Qin, Bo An and Tie-Yan Liu, Revenue Maximization for Finitely Repeated Ad Auctions, AAAI 2017.
Jia Zhang, Weidong Ma, Tao Qin, Xiaoming Sun and Tie-Yan Liu, Randomized Mechanisms for Selling Reserved Instances in Cloud Computing, AAAI 2017.
Xiang Li, Tao Qin, Jian Yang, and Tie-Yan Liu, LightRNN: Memory and Computation-Efficient Recurrent Neural Networks, NIPS 2016.
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma, Dual Learning for Machine Translation, NIPS 2016.
Xujin Chen, Xiaodong Hu, Tie-Yan Liu, Weidong Ma, Tao Qin, Pingzhong Tang, Changjun Wang, Bo Zheng, Efficient Mechanism Design for Online Scheduling, accepted by Journal of Artificial Intelligence Research (JAIR), 2016.
Jiang Rong, Tao Qin, Bo An, Tie-Yan Liu, Modeling Bounded Rationality for Sponsored Search Auctions, ECAI 2016.
Yingce Xia, Tao Qin, Weidong Ma, Nenghai Yu, Tie-Yan Liu, Budgeted Multi-armed Bandits with Multiple Plays, IJCAI 2016. [full version]
Yingce Xia, Tao Qin, Nenghai Yu, Tie-Yan Liu, Best Action Selection in a Stochastic Environment, AAMAS 2016.
Tie-Yan Liu, Weidong Ma, Pingzhong Tang, Tao Qin, Guang Yang, Bo Zheng, Online Non-Preemptive Story Scheduling in Web Advertising, AAMAS 2016.
Jiang Rong, Tao Qin, Bo An, Tie-Yan Liu, Optimal Sample Size for Adword Auctions, AAMAS 2016, short paper.
Bo Zheng, Li Xiao, Guang Yang, Tao Qin, Online Posted-Price Mechanism with a Finite Time Horizon, AAMAS 2016, short paper.
Qizhen Zhang, Haoran Wang, Yang Chen, Tao Qin, Ying Yan, Thomas Moscibroda, A Shapley Value Approach for Cost Allocation in the Cloud, SOCC 2015, poster.
Yingce Xia, Haifang Li, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Thompson Sampling for Budgeted Multi-armed Bandits, IJCAI 2015.
Bolei Xu, Tao Qin, Guoping Qiu, and Tie-Yan Liu, Optimal Pricing for the Competitive and Evolutionary Cloud Market, IJCAI 2015.
Changjun Wang, Weidong Ma, Tao Qin, Xujin Chen, Xiaodong Hu, Tie-Yan Liu, Selling Reserved Instances in Cloud Computing, IJCAI 2015.
Long Tran-Thanh, Yingce Xia, Tao Qin, Nick Jenning, Efficient Algorithms with Performance Guarantees for the Stochastic Multiple-Choice Knapsack Problem, IJCAI 2015.
Bnyi Chen, Tao Qin, and Tie-Yan Liu, Mechanism Design for Daily Deals, AAMAS 2015.
Changjun Wang, Weidong Ma, Tao Qin, Feidiao Yang, Xujin Chen, Xiaodong Hu, and Tie-Yan Liu, New Mechanisms for Selling Reserved Instances in Cloud Computing, AAMAS 2015, short paper.
Bolei Xu, Tao Qin, Guoping Qiu, Tie-Yan Liu, Competitive Pricing for Cloud Computing in Evolutionary Market, AAMAS 2015, short paper.
Jiang Rong, Tao Qin, and Bo An. Quantal Response Equilibrium for Sponsored Search Auctions, AAMAS 2015, short paper.
Hafang Li, Fei Tian, Wei Chen, Tao Qin, Zhi-Ming Ma, and Tie-Yan Liu, Generalization Analysis for Game-Theoretic Machine Learning, AAAI 2015.
Tie-Yan Liu, Wei Chen, and Tao Qin, Mechanism Learning with Mechanism Induced Data, AAAI 2015.
Junpei Komiyama and Tao Qin, Time-Decaying Bandits for Non-stationary Systems, WINE 2014.
Tao Qin, Wei Chen, and Tie-Yan Liu. Sponsored Search Auctions: Recent Advances and Future Directions, ACM Transactions on Intelligent Systems and Technology, 2014.
Jiang Rong, Tao Qin, and Bo An. Quantal Response Equilibrium for Sponsored Search Auctions: Computation and Inference, Ad Auctions 2014, in conjunction with EC 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.
Yingce Xia, Tao Qin, and Tie-Yan Liu. Incentivizing High-quality Content from Heterogeneous Users: On the Existence of Nash Equilibrium, AAAI 2014.
Fei Tian, Haifang Li, Wei Chen, Tao Qin, and Tie-Yan Liu. Agent Behavior Prediction and Its Generalization Analysis, AAAI 2014.
Weidong Ma, Tao Qin, and Tie-Yan Liu, Generalized Second Price Auctions with Value Externalities, AAMAS 2014. [poster]
Weihao Kong, Jian Li, Tao Qin, and Tie-Yan Liu, Optimal Allocation for Chunked-Reward Advertising, WINE 2013.
Wenkui Ding, Tao Wu, Tao Qin, and Tie-Yan Liu, Price of Anarchy for Generalized Second Price Auction, arXiv preprint arXiv:1305.5404.
Min Xu, Tao Qin, and Tie-Yan Liu, Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising, NIPS 2013.
Wenkui Ding, Tao Qin, Xu-Dong Zhang and Tie-Yan Liu, Multi-Armed Bandit with Budget Constraint and Variable Costs, AAAI 2013.
Xiubo Geng, Tao Qin, Xue-Qi Cheng and Tie-Yan Liu, A Noise-Tolerant Graphical Model for Ranking, Information Processing and Management, 2012.
Sungchul Kim, Tao Qin, Hwanjo Yu and Tie-Yan Liu, An Advertiser-Centric Approach to Understand User Click Behavior in Sponsored Search, CIKM 2011.
Xiubo Geng, Tie-Yan Liu, Tao Qin, Xue-Qi Cheng and Hang Li, Selecting Optimal Training Data for Learning to Rank, Information Processing and Management, 2011.
Tao Qin, Xiu-Bo Geng and Tie-Yan Liu, A New Probabilistic Model for Rank Aggregation, NIPS 2010.
Wenkui Ding, Tao Qin and Xu-Dong Zhang, Learning to Rank with Supplementary Data, AIRS 2010.
Yajuan Duan, Long Jiang, Tao Qin, Ming Zhou and Harry Shum. An Empirical Study on Learning to Rank of Tweets, COLING 2010.
Jiang Bian, Tie-Yan Liu, Tao Qin, Hongyuan Zha. Ranking with Query-Dependent Loss for Web Search, WSDM 2010.
Tao Qin, Tie-Yan Liu, and Hang Li, A General Approximation Framework for Direct Optimization of Information Retrieval Measures, Information Retrieval Journal, 2010. [Technique report]
Tao Qin, Tie-Yan Liu, Jun Xu, and Hang Li, LETOR: A Benchmark Collection for Research on Learning to Rank for Information Retrieval, Information Retrieval Journal, 2010. [pdf]
Zhengya Sun, Tao Qin, Jue Wang, Qing Tao. Robust Sparse Rank Learning for Non-Smooth Ranking Measures, SIGIR 2009.
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Hang Li. Global Ranking Using Continuous Conditional Random Fields, NIPS 2008. [Oral Paper] [Technique report][bibtex]
Yan-Yan Lan, Tie-Yan Liu, Tao Qin, Zhi-Ming Ma, Hang Li. Query-Level Stability and Generalization in Learning to Rank, ICML 2008.
Xiu-Bo Geng, Tie-Yan Liu, Tao Qin, Andrew Arnold, Hang Li, Heung-Yeung Shum. Query Dependent Ranking Using K-Nearest Neighbor, SIGIR 2008.
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Wenying Xiong, Hang Li. Learning to Rank Relational Objects and Its Application to Web Search, WWW 2008. [slides]
Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng Wang, Tie-Yan Liu, and Hang Li. Query-level Loss Functions for Information Retrieval. Information Processing and Management, 2008. [DOI]
Tie-Yan Liu, Jun Xu, Tao Qin, Wenying Xiong, Hang Li. LETOR: Benchmarking “Learning to Rank for Information Retrieval”, SIGIR 2007 workshop: Learning to Rank for Information Retrieval.
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Hang Li. Learning to Rank: From Pairwise Approach to Listwise Approach, ICML 2007.
Tao Qin, Tie-Yan Liu, Wei Lai, Xu-Dong Zhang, De-Sheng Wang, Hang Li. Ranking with Multiple Hyperplanes, SIGIR 2007.
Xiubo Geng, Tie-Yan Liu, Tao Qin, Hang Li. Feature Selection for Ranking, SIGIR 2007.
Mingfeng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, Wei-Ying Ma. FRank: A Ranking Method with Fidelity Loss, SIGIR 2007.
Yu-Ting Liu, Tie-Yan Liu, Tao Qin, Zhi-Ming Ma, Hang Li. Supervised Rank Aggregation, WWW 2007.
Bin Gao, Tie-Yan Liu, Tao Qin, Xin Zheng, Qian-Sheng Cheng, Wei-Ying Ma. Web Image Clustering by Consistent Utilization of Low-level Features and Surrounding Texts, ACM Multimedia 2005.
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, Zheng Chen, Wei-Ying Ma. A Study of Relevance Propagation for Web Search, SIGIR 2005.


Microsoft Learning to Rank Datasets with tens of thousands of queries and millions of documents have been released. If you find any problems or have any suggestions, please let us know.
LETOR: the first public learning to rank data collection. Reference paper & Bibtex

Professional Activities
Senior PC member, AAMAS 2016, ACML 2016
PC member, AAMAS 2015, KDD 2015, EC 2015, AAAI 2015, Ad Auctions Workshop (2015), Autonomous Agents and Multi-Agent Systems at Scale Workshop 2015
Senior PC member, ACML 2015
PC member, NIPS 2014, CIKM 2014, WSDM 2014
Area Chair, SIGIR 2013
PC member, SDM 2013, EC 2013, Big Data 2013, AIRS 2013
Co-Chair, KDD 2012 Workshop: ADKDD 2012.
PC member, CIKM 2012, ACML 2012, ADMA 2012, AIRS 2012
Area Chair, SIGIR 2012
Co-Chair, SIGIR 2011 Workshop: Internet Advertising.
PC member, WWW 2011, SIGIR 2011, CIKM 2011, ADMA 2011
Co-Chair, NIPS 2010 Workshop: Machine Learning in Online Advertising
PC member, SIGIR 2009/2010, SIGMAP 2009, EMNLP 2008



Mechanism design for complex systems

Established: August 1, 2016

We are living in and interacting with many complex systems nowadays, including the advertising system, cloud system, large scale machine learning system, and multitenancy big data system. There are multiple kinds of players with different objectives and there could be millions of players in total. Designing good mechanisms is important for those systems to achieve efficiency, revenue, and stability. We combine machine learning, user modeling, and game theory techniques together to design mechanisms for those…

Reinforcement learning for Internet applications

Established: August 1, 2016

Reinforcement learning (RL) has achieved great success in video and board games, in which RL can make improvement through (almost) infinite self-play. However, different from games, in real-world applications,  self-play is either impossible or costly. In this project, our goal is to make RL feasible and successful in real-world applications. Our targeted applications include machine translation, chat bot, and search advertising.

Reinforcement learning for machine learning

Established: August 1, 2016

Reinforcement learning (RL) has achieved great success in video and board games. In this project, we aim at boosting machine learning algorithms and systems by leveraging reinforcement learning techniques. We focus the following aspects. First, RL for data selection and pre-processing, in which we use RL techniques to select right data at right time and process the data in a right way for model training. Second, RL for hyper parameter optimization. Setting appropriate hyper parameters…

Machine Learning Theory

Established: August 1, 2016

Theory plays an important role in improving existing algorithms and motivating new algorithms. In this project, we aim to give mathematical formulations, derive theoretical results, and design new algorithms to variants of machine learning problems. In the past several years, we have investigated or are investigating the following topics: Distributed machine learning algorithms Deep neural networks Game-theoretic machine learning Learning to rank

Microsoft Learning to Rank Datasets

Established: June 10, 2010

We released two large scale datasets for research on learning to rank: MSLR-WEB30k with more than 30,000 queries and a random sampling of it MSLR-WEB10K with 10,000 queries.   Dataset Descriptions The datasets are machine learning data, in which queries and urls are represented by IDs. The datasets consist of feature vectors extracted from query-url pairs along with relevance judgment labels: (1) The relevance judgments are obtained from a retired labeling set of a commercial…

LETOR: Learning to Rank for Information Retrieval

Established: January 1, 2009

Overview This website is designed to facilitate research in LEarning TO Rank (LETOR). Much information about learning to rank can be found in the website, including benchmark datasets, public baselines, published papers, research communities, and passed and coming events. Microsoft Learning to Rank Datasets have been released. Two large datasets with tens of thousands of queries (30,000+/10,000) were released. 136 features have been extracted for each query-url pair. The datasets can be downloaded at Microsoft…