Tie-Yan Liu

Overview

Tie-Yan Liu is a senior researcher and research manager at Microsoft Research Asia. His research interests include machine learning (learning to rank, online learning, deep learning, and statistical learning theory), information retrieval, data mining, computational advertising, and algorithmic game theory. He is well known for his pioneer work on learning to rank for information retrieval. He has authored the first book in this area, and published tens of impactful papers on both algorithms and theorems of learning to rank (with around 7500 citations in the past few years). He has also published extensively on other related topics. In particular, his paper on graph mining won the best student paper award of SIGIR (2008); his paper on video shot boundary detection won the most cited paper award of the Journal of Visual Communication and Image Representation (2004-2006); and his work on Internet economics won the research break-through award of Microsoft Research Asia (2012). Tie-Yan is very active in serving the research community. He is a program committee co-chair of SocInfo (2015), ACML (2015), WINE (2014), AIRS (2013), and RIAO (2010), a local co-chair of ICML 2014, a tutorial co-chair of SIGIR (2016) and WWW (2014), a doctorial consortium co-chair of WSDM (2015), a demo/exhibit co-chair of KDD (2012), and an area/track chair or senior program committee member of many conferences including KDD (2015), ACML (2014), SIGIR (2008-2011), AIRS (2009-2011), and WWW (2011, 2015). He is an associate editor of ACM Transactions on Information System (TOIS), an editorial board member of Information Retrieval Journal (IRJ) and Foundations and Trends in Information Retrieval (FnTIR). He is a keynote speaker at ECML/PKDD (2014), ORSC (2014), CCIR (2011, 2014), CCML (2013), and PCM (2010), a tutorial speaker at SIGIR (2008, 2010, 2012), WWW (2008, 2009, 2011), and KDD (2012), and a plenary panelist at KDD (2011). He is a senior member of the IEEE and the ACM, as well as a senior member and distinguished speaker of the CCF. He is currently an adjunct professor of Carnegie Mellon University (LTI), Nankai University, Sun Yat-Sen University, and University of Science and Technology of China; and an Honorary Professor of University of Nottingham.

Please refer to his CV for more information.

       

Email: tyliu@microsoft.com
Mailing address: Tower 2, No.5, Danling Street, Haidian District, Beijing, 100080, P. R. China.
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"Tie-Yan Liu: at Sina Microblog
"Tie-Yan Liu" on Google Scholar
"Tie-Yan Liu" on Microsoft Academic Search
"Tie-Yan Liu" on DBLP

News

  • WINE 2014 will be held in Beijing.
  • Hiring: We are hiring at all levels (from fresh graduates to experienced researchers)! If your major is machine learning, information retrieval, or algorithmic game theory, and you have the passion to change the world, please send your resume to tyliu@microsoft.com.

 

Representative Publications

  1. Tao Qin, Wei Chen, and Tie-Yan Liu, Sponsored Search Auctions: Recent Advances and Future Directions, ACM Transactions on Intelligent Systems and Technology (TIST), 2014.
  2. Chang Xu, Yanlong Bai, Jiang Bian, Bin Gao, Gang Wang, Xiaoguang Liu, and Tie-Yan Liu, RC-Net: A General Framework for Incorporating Knowledge into Word Representations, CIKM 2014.
  3. Fei Tian, Jiang Bian, Bin Gao, Hanjun Dai, Rui Zhang, and Tie-Yan Liu, A Scalable Probabilistic Model for Learning Multi-Prototype Word Embedding, COLING 2014.
  4. Siyu Qiu, Qing Cui, Jiang Bian, Bin Gao, and Tie-Yan Liu, Co-learning of Word Representations and Morpheme Representations, COLING 2014.
  5. Bin Gao, Jiang Bian, and Tie-Yan Liu, Knowledge Powered Deep Learning for Word Embedding, ECML 2014.
  6. Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, Liwei Wang, Generalized Second Price Auction with Probabilistic Broad Match, EC 2014.
  7. Yingce Xia, Tao Qin and Tie-Yan Liu, Incentivizing High-quality Content from Heterogeneous Users: On the Existence of Nash Equilibrium, AAAI 2014.
  8. Fei Tian, Haifang Li, Wei Chen, Tao Qin and Tie-Yan Liu, Agent Behavior Prediction and Its Generalization Analysis, AAAI 2014.
  9. Fei Tian, Bin Gao and Tie-Yan Liu, Learning Deep Representations for Graph Clustering, AAAI 2014.
  10. Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang and Tie-Yan Liu, Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks, AAAI 2014.
  11. Tie-Yan Liu, Weidong Ma, Tao Qin, and Tao Wu, Generalized Second Price Auctions with Value Externalities, AAMAS 2014.
  12. Jiang Bian, Taifeng Wang, and Tie-Yan Liu, Sampling Dilemma: Towards Effective Data Sampling for Click Prediction in Sponsored Search, WSDM 2014.
  13. Ying Zhang, Weinan Zhang, Bin Gao, Xiaojie Yuan, and Tie-Yan Liu, Bid Keyword Suggestion in Sponsored Search based on Competitiveness and Relevance, Information Processing and Management, 2014.
  14. Weihao Kong, Jian Li, Tie-Yan Liu and Tao Qin, Optimal Allocation for Chunked-Reward Advertising, WINE 2013
  15. Haifeng Xu, Diyi Yang, Bin Gao and Tie-Yan Liu, Predicting Advertiser Bidding Behaviors in Sponsored Search by Rationality Modeling, WWW 2013.
  16. Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen, and Tie-Yan Liu, A Theoretical Analysis of NDCG Type Ranking Measures, COLT 2013.
  17. Di He, Wei Chen, Liwei Wang, Tie-Yan Liu, A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search, IJCAI 2013.
  18. Wenkui Ding, Tao Qin, and Tie-Yan Liu, Multi-Armed Bandit with Budget Constraint and Variable Costs, AAAI 2013.
  19. Min Xu, Tao Qin, and Tie-Yan Liu, Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising, NIPS 2013.
  20. Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu, Online Learning for Auction Mechanism in Bandit Setting, Decision Support Systems, 2013.
  21. Taifeng Wang, Jiang Bian, Shusen Liu, Yuyu Zhang, and Tie-Yan Liu, Psychological Advertising: Exploring Consumer Psychology for Click Prediction in Sponsored Search, KDD 2013.
  22. Lei Yao, Wei Chen and Tie-Yan Liu, Convergence Analysis for Weighted Joint Strategy Fictitious Play in Generalized Second Price Auction, WINE 2012.
  23. Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Tie-Yan Liu, Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space. NIPS 2012.
  24. Weinan Zhang, Ying Zhang, Bin Gao, Yong Yu, Xiaojie Yuan, and Tie-Yan Liu, Joint optimization of bid and budget allocation in sponsored search, KDD 2012.
  25. Konstantin Salomatin, Tie-Yan Liu, and Yiming Yang, A Unified Optimization Framework for Auction and Guaranteed Delivery in Online Advertising, CIKM 2012.
  26. Chenyan Xiong, Taifeng Wang, Wenkui Ding, Yidong Shen, Tie-Yan Liu. Relational Click Prediction for Sponsored Search , WSDM 2012.
  27. Tie-Yan Liu. Learning to Rank for Information Retrieval, Springer, 2011.
  28. Olivier Chapelle, Yi Chang, and Tie-Yan Liu, Future research directions on learning to rank, Proceeding track, Journal of Machine Learning Research, 2011.
  29. Sungchul Kim, Tao Qin, Hwanjo Yu and Tie-Yan Liu, An Advertiser-Centric Approach to Understand User Click Behavior in Sponsored Search, CIKM 2011.
  30. Bin Gao, Tie-Yan Liu, Taifeng Wang, Wei Wei, and Hang Li, Semi-supervised graph ranking with rich meta data, KDD 2011.
  31. Zhicong Cheng, Bin Gao, Congkai Sun, Yanbing Jiang, and Tie-Yan Liu. Let Web Spammers Expose Themselves, WSDM 2011.
  32. Bin Gao, Tie-Yan Liu, Yuting Liu, Taifeng Wang, Zhiming Ma, and Hang Li, Page Importance Computation based on Markov Processes, Information Retrieval Journal, 2011.
  33. Xiubo Geng, Tao Qin, Xueqi Cheng, Tie-Yan Liu, A Noise-Tolerant Graphical Model for Ranking, Information Processing and Management, 2011.
  34. Xiubo Geng, Tie-Yan Liu, Tao Qin, Xueqi Cheng, Hang Li, Selecting Optimal Training Data for Learning to Rank, Information Processing and Management, 2011.
  35. Tao Qin, Xiubo Geng, and Tie-Yan Liu, A New Probabilistic Model for Rank Aggregation, NIPS 2010.
  36. Wei Chen, Tie-Yan Liu, Zhiming Ma, Two-Layer Generalization Analysis for Ranking Using Rademacher Average, NIPS 2010.
  37. Zhicong Cheng, Bin Gao, and Tie-Yan Liu, Actively Predicting Diverse Search Intent from User Browsing Behaviors, WWW 2010.
  38. Jiang Bian, Tie-Yan Liu, Tao Qin, and Hongyuan Zha, Ranking with query-dependent loss for web search. WSDM 2010.
  39. Yin He and Tie-Yan Liu, Tendency Correlation Analysis for Direct Optimization of Evaluation Measures in Information Retrieval, Information Retrieval Journal, 2010.
  40. Tao Qin, Tie-Yan Liu, and Hang Li, A General Approximation Framework for Direct Optimization of Information Retrieval Measures, Information Retrieval Journal, 2009.
  41. 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, 2009.
  42. Tie-Yan Liu. Learning to Rank for Information Retrieval, Foundation and Trends on Information Retrieval, Now Publishers, 2009.
  43. Yuting Liu, Tie-Yan Liu, Zhiming Ma, and Hang Li. A Framework to Compute Page Importance based on User Behaviors, Information Retrieval Journal, 2009.
  44. Yanyan Lan, Tie-Yan Liu, Zhiming Ma, and Hang Li. Generalization Analysis of Listwise Learning to Rank Algorithms, ICML 2009.
  45. Fen Xia, Tie-Yan Liu, Hang Li, Statistical Consistency of Top-k Ranking, NIPS 2009.
  46. Wei Chen, Tie-Yan Liu, Yanyan Lan, Zhiming Ma, Hang Li, Ranking Measures and Loss Functions in Learning to Rank, NIPS 2009.
  47. Tao Qin, Tie-Yan Liu, Xudong Zhang, and Hang Li. Global Ranking Using Continuous Conditional Random Fields, NIPS 2008.
  48. Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. Listwise Approach to Learning to Rank: Theorem and Algorithm, ICML 2008.
  49. Yanyan Lan, Tie-Yan Liu, Tao Qin, Zhiming Ma, and Hang Li. Query-level Stability and Generalization in Learning to Rank, ICML 2008.
  50. Xiubo Geng, Tie-Yan Liu, Tao Qin, Andrew Arnold, Hang Li, and Heung-Yeung Shum. Query-dependent Ranking using K-Nearest Neighbor, SIGIR 2008.
  51. Jun Xu, Tie-Yan Liu, Min Lu, Hang Li, and Wei-Ying Ma. Directly Optimizing IR Evaluation Measures in Learning to Rank, SIGIR 2008.
  52. Tao Qin, Tie-Yan Liu, Jun Xu, and Hang Li. Making LETOR More Useful and Reliable, LR4IR 2008, in conjunction with SIGIR 2008.
  53. Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Wen-Ying Xiong, and Hang Li. Learning to Rank Relational Objects and Its Application to Web Search, WWW 2008.
  54. Yuting Liu, Bin Gao, Tie-Yan Liu, Ying Zhang, Zhiming Ma, Shuyuan He, and Hang Li. BrowseRank: Letting Web Users Vote for Page Importance, SIGIR 2008. [SIGIR Best Student Paper Award]
  55. Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. Learning to Rank: From Pairwise Approach to Listwise Approach. ICML 2007.
  56. Xiubo Geng, Tie-Yan Liu, Tao Qin, and Hang Li. Feature Selection for Ranking, SIGIR 2007.
  57. Mingfeng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma. FRank: A Ranking Method with Fidelity Loss, SIGIR 2007.
  58. Tao Qin, Tie-Yan Liu, Wei Lai, Xu-Dong Zhang, De-Sheng Wang, and Hang Li. Ranking with Multiple Hyperplanes, SIGIR 2007.
  59. Tie-Yan Liu, Jun Xu, Tao Qin, Wenying Xiong, and Hang Li. LETOR: Benchmark dataset for research on learning to rank for information retrieval, LR4IR 2007, in conjunction with SIGIR 2007.
  60. Yuting Liu, Tie-Yan Liu, Tao Qin, Zhi-Ming Ma, and Hang Li. Supervised Rank Aggregation, WWW 2007.
  61. Ying Bao, Guang Feng*, Tie-Yan Liu, Zhiming Ma, and Ying Wang. Ranking Websites: A Probabilistic View, Internet Mathematics, 2007.
  62. Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng Wang, Tie-Yan Liu, and Hang Li. Query-level Loss Function for Information Retrieval. Information Processing and Management, 2007.
  63. Tao Qin, Xu-Dong Zhang, Tie-Yan Liu, De-Sheng Wang, Hong-Jiang Zhang. An Active Feedback Framework for Image Retrieval, Pattern Recognition Letters, 2007.
  64. Yunbo Cao, Jun Xu, Tie-Yan Liu, Hang Li, Yalou Huang and Hsiao-Wuen Hon. Adapting Ranking SVM to Document Retrieval, SIGIR 2006.
  65. Guang Feng, Tie-Yan Liu, Ying Wang, Ting Bao, Zhiming Ma, Xu-Dong Zhang, and Wei-Ying Ma. AggregateRank: Bringing Order to Websites, SIGIR 2006.
  66. Qiankun Zhao, Chuhong Hoi, Tie-Yan Liu, Sourav Bhowmick, Michael Lyu, and Wei-Ying Ma. Time-Dependent Semantic Similarity Measure of Queries Using Historical Click-Through Data, WWW 2006.
  67. Qiankun Zhao, Tie-Yan Liu, Sourav Bhowmick, and Wei-Ying Ma. Event Detection from Evolution of Click-through Data, KDD 2006.
  68. Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, Guang Geng, De-Sheng Wang, and Wei-Ying Ma. Topic Distillation Via Subsite Retrieval, Information Processing and Management, 2006.
  69. Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Zheng Chen, and Wei-Ying Ma. A Study on Relevance Propagation for Web Search, SIGIR 2005.
  70. Bin Gao, Tie-Yan Liu, Xin Zheng, Qian-Sheng Cheng, and Wei-Ying Ma. Consistent Bipartite Graph Co-Partitioning for Star-Structured High-Order Heterogeneous Data Co-Clustering, KDD 2005.
  71. Bin Gao, Tie-Yan Liu, Guang Feng, Tao Qin, Qian-Sheng Cheng, and Wei-Ying Ma. Hierarchical Taxonomy Preparation for Text Categorization Using Consistent Bipartite Spectral Graph Co-partitioning, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2005.
  72. Tie-Yan Liu, Yiming Yang, Hao Wan, Hua-Jun Zeng, Zheng Chen, and Wei-Ying Ma. Support Vector Machines Classification with Very Large Scale Taxonomy, SIGKDD Explorations, 2005.
  73. Tie-Yan Liu, Kwok-Tung Lo, Xu-Dong Zhang, and Jian Feng. A New Cut Detection Algorithm with Constant False-Alarm Ratio for Video Segmentation, Journal of Visual Communications and Image Representation, 2004. [Most Cited Paper Award]
  74. Tie-Yan Liu, Xu-Dong Zhang, Jian Feng, and Kwok-Tung Lo. Shot Reconstruction Degree: a Novel Criterion for Key Frame Selection, Pattern Recognition Letters, 2004.
  75. Tie-Yan Liu, Kwok-Tung Lo, Jian Feng, and Xu-Dong Zhang. Frame Interpolation Scheme Using Inertia Motion Prediction. Signal Processing: Image Communication, 2003.
  76. Tie-Yan Liu, Jian Feng, Xu-Dong Zhang, and Kwok-Tung Lo. Inertia-based Cut Detection and Its Integration with Video Coder. IEE Proceedings on Vision, Image and Signal Processing, 2003.

 

Professional Activities

  • PC Co-Chair, SocInfo 2015, ACML 2015, WINE 2014, AIRS 2013, RIAO 2010.
  • Tutorial Co-Chair, SIGIR 2016, WWW 2014
  • Doctorial Consortium Co-Chair, WSDM 2015.
  • Local Co-Chair, ICML 2014.
  • Demo/exhibition Co-Chair, KDD 2012.
  • Track Chair / Area Chair/ Senior PC member, KDD 2015, WWW 2015, ACML 2014, IJCAI 2013, WWW 2011. SIGIR 2008-2011, AIRS 2009-2011.
  • Associate Editor, ACM Transactions on Information System.
  • Editorial Board Member, Information Retrieval Journal, and Foundations and Trends in Information Retrieval.
  • Guest Editor, Special issue on Learning to Rank for IR, Information Retrieval Journal; Special issue on Learning to Rank Challenge, Journal of Machine Learning Research.
  • Tutorial speaker, KDD 2012, SIGIR 2012, WWW 2011, SIGIR 2010, WWW 2009, WWW 2008, SIGIR 2008.
  • Keynote speaker, ECML/PKDD 2014, ORSC 2014, CCIR 2014, CCML 2013, PCM 2010, CCIR 2011.
  • Plenary Panelist, KDD 2011.
  • Workshop Co-chair, KDD Workshop on Internet Economics and Online Advertising (ADKDD), 2012; SIGIR Workshop on Online Advertising, 2011; NIPS Workshop on Machine Learning in Online Advertising, 2010; ICML Workshop on Learning to Rank, 2010; SIGIR Workshop on Learning to Rank, 2007-2009.
  • Regularly serve as program committee member / reviewer for many leading international conferences, including SIGIR, NIPS, ICML, KDD, AAAI, WWW, WSDM, SDM, ICDM, CIKM, ECIR, ACL, ICIP, etc.
  • Senior member of IEEE, ACM, and CCF.
  • Distinguished speaker of CCF.
  • Adjunct professor of the Carnegie Mellon University (LTI), Nankai University, University of Science and Technology of China, and Sun Yat-Sen University
  • Honorary Professor of University of Nottingham.

 

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