Machine Learning Group

[Book]

• Tie-Yan Liu. Learning to Rank for Information Retrieval, Springer, 2011.

[Journal Papers]

• Shuaiqiang Wang, Shanshan Huang, Tie-Yan Liu, Jun Ma, Zhumin Chen, Jari Veijalainen, Ranking-oriented Collaborative Filtering: A Listwise Approach, ACM Transactions on Information Systems, 2016
• Xujin Chen, Xiaodong Hu, Tie-Yan Liu, Weidong Ma, Tao Qin, Pingzhong Tang, Changjun Wang, and Bo Zheng, Efficient Mechanism Design for Online Scheduling, Journal of Artificial Intelligence Research, 2016.
• Chang Xu, Gang Wang, Xiaoguang Liu, Tie-Yan Liu, Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks, IEEE Transactions on Computers, 2016.
• Wei Chen, Tie-Yan Liu, and Xinxin Yang, Reinforcement Learning Behaviors in Sponsored Search, Applied Stochastic Models in Business and Industry, 2016.
• Qing Cui, Bin Gao, Jiang Bian, Hanjun Dai, and Tie-Yan Liu, KNET: A General Framework for Learning Word Embedding using Morphological Knowledge, ACM Transactions on Information Systems, 2015.
• Wei Wei, Bin Gao, Tie-Yan Liu, Taifeng Wang, Guohui Li, and Hang Li, A Ranking Approach on Large-scale Graph with Multi-dimensional Heterogeneous Information, IEEE Transactions on Cybernetics, 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.
• 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.
• Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu, Online Learning for Auction Mechanism in Bandit Setting, Decision Support Systems, 2013
• Bin Gao, Tie-Yan Liu, Yuting Liu, Taifeng Wang, Zhiming Ma, and Hang Li, Page Importance Computation based on Markov Processes, Information Retrieval, 2011.
• Olivier Chapelle, Yi Chang, and Tie-Yan Liu, Future research directions on learning to rank, Proceeding track, Journal of Machine Learning Research, 2011.
• Xiubo Geng, Tie-Yan Liu, Tao Qin, Xueqi Cheng, Hang Li, Selecting Optimal Training Data for Learning to Rank, Information Processing and Management, 2011.
• Yin He and Tie-Yan Liu, Tendency Correlation Analysis for Direct Optimization of Evaluation Measures in Information Retrieval, Information Retrieval, 2010.
• Tie-Yan Liu, Thorsten Joachims, Hang Li, and Chengxiang Zhai, Introduction to special issue on learning to rank for information retrieval, Information Retrieval, 2010.
• Tie-Yan Liu. Learning to Rank for Information Retrieval, Foundations and Trends in Information Retrieval, 2009.
• Yuting Liu, Tie-Yan Liu, Zhiming Ma, and Hang Li. A Framework to Compute Page Importance based on User Behaviors, Information Retrieval, 2009.
• Tao Qin, Tie-Yan Liu, and Hang Li, A General Approximation Framework for Direct Optimization of Information Retrieval Measures, Information Retrieval, 2009.
• 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, 2009
• 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.
• 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.
• Ying Bao, Guang Feng, Tie-Yan Liu, Zhiming Ma and Ying Wang. Ranking Websites: A Probabilistic View, Internet Mathematics, 2007.
• 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.
• Bin Gao, Tie-Yan Liu, Xin Zheng, Qiansheng 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, 2005.
• 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.
• Tie-Yan Liu, Kwoktung 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. 
• Tie-Yan Liu, Xu-Dong Zhang, Jian Feng, and Kwoktung Lo. Shot Reconstruction Degree: a Novel Criterion for Key Frame Selection, Pattern Recognition Letters, 2004.
• Tie-Yan Liu, Kwoktung Lo, Xu-Dong Zhang, and Jian Feng. Frame Interpolation Scheme Using Inertia Motion Prediction. Signal Processing: Image Communication, 2003.
• Tie-Yan Liu, Kwoktung Lo, Xu-Dong Zhang, and Jian Feng. Inertia-based Cut Detection and Its Integration with Video Coder. IEE Proceedings on Vision, Image and Signal Processing, 2003.

[Conference Papers]

2017:

• Yingce  Xia ,  Fei  Tian, Lijun  Wu ,  Jianxin  Lin ,  Tao  Qin , and Tie-Yan  Liu, Deliberation Networks: Sequence Generation Beyond One-Pass Decoding, NIPS 2017.
• Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, and Tie-Yan Liu, Decoding with Value Networks for Neural Machine Translation, NIPS 2017.
• Guolin Ke, Qi Meng, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu, LightGBM: A Highly Efficient Gradient Boosting Decision Tree, NIPS 2017.
• Yue Wang, Wei Chen, Yuting Liu, and Tie-Yan Liu, Finite Sample Analysis of GTD Policy Evaluation Algorithms in Markov Setting, NIPS 2017,
• Yingce Xia, Tao Qin, Wei Chen, Tie-Yan Liu, Dual Supervised Learning, ICML 2017.
• Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, and Tie-Yan Liu, Asynchronous Stochastic Gradient Descent with Delay Compensation, ICML2017.
• 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.
• Quanming Yao, James Kwok, Fei Gao, Wei Chen, and Tie-Yan Liu, Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems, IJCAI 2017.
• Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhi-Ming Ma and Tie-Yan Liu, Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction, AAAI 2017.
• Qi Meng, Yue Wang, Wei Chen, Taifeng Wang, Zhi-Ming Ma and Tie-Yan Liu, Generalization Error Bounds for Optimization Algorithms via Stability, AAAI 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, AAAI2017.
• Shizhao Sun, Wei Chen, Jiang Bian, and Tie-Yan Liu, Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks, ECML2017.
• Yingce Xia,Fei Tian, Tao Qin, Tie-Yan Liu, Sequence Generation with Target Attention, ECML 2017.

2016:

• Xiang Li, Tao Qin, and Tie-Yan Liu, 2-Component Recurrent Neural Networks, NIPS 2016
• Di He, Yingce Xia, Tao Qin, Tie-Yan Liu, and Wei-Ying Ma, Machine Translation Through Learning From a Communication Game, NIPS 2016
• Qi Meng, Guolin Ke, Qiwei Ye, Taifeng Wang, Wei Chen, and Tie-Yan Liu, PV-Tree: A Communication-Efficient Parallel Algorithm for Decision Tree, NIPS 2016
• Huazheng Wang, Fei Tian, Bin Gao, Chenjieren Zhu, Jiang Bian, Tie-Yan Liu, Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding, EMNLP 2016.
• Yiren Wang, Fei Tian, Recurrent Residual Learning for Sequence Classification,  EMNLP 2016, short paper.
• Yingce Xia, Tao Qin, Weidong Ma, Nenghai Yu and Tie-Yan Liu, Budgeted Multi-armed Bandits with Multiple Plays, IJCAI 2016.
• Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang and Tie-Yan Liu, Asynchronous Accelerated Stochastic Gradient Descent, IJCAI 2016.
• Hongbin Ma, Bin Shao, Yanghua Xiao, Liang Jeff Chen, Haixun Wang. G-SQL: Fast Query Processing via Graph Exploration. PVLDB 2016.
• Yingce Xia, Tao Qin, 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.
• Bo Zheng, Li Xiao, Guang Yang, Tao Qin, Online Posted-Price Mechanism with a Finite Time Horizon, AAMAS 2016, short paper.
• Shizhao Sun, Wei Chen, Liwei Wang, and Tie-Yan Liu, On the Depth of Deep Neural Networks: A Theoretical View, AAAI 2016.

2015:

• 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, Competitive Pricing for Cloud Computing in an Evolutionary Market, IJCAI 2015.
• Changjun Wang, Weidong Ma, Tao Qin, Xujin Chen, Xiaodong Hu, and 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.
• Yitan Li, Linli Xu, Fei Tian, Liang Jiang, Xiaowei Zhong and Enhong Chen, Word Embedding Revisited: A New Representation Learning and Explicit Matrix Factorization Perspective, IJCAI 2015.
• Shanshan Huang, Shuaiqiang Wang, Tie-Yan Liu, Jun Ma, Zhumin Chen, and Jari Veijalainen, Listwise Collaborative Filtering, SIGIR 2015.
• Binyi Chen, Tao Qin, and Tie-Yan Liu, Mechanism Design for Daily Deals, AAMAS 2015.
• Jiang Rong, Tao Qin, and Bo An. Quantal Response Equilibrium for Sponsored Search Auctions, AAMAS 2015, short paper.
• Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric Xing, Tie-Yan Liu, and Wei-Ying Ma, LightLDA: Big Topic Models on Modest Computer Cluster, WWW 2015.
• Tie-Yan Liu, Wei Chen, and Tao Qin, Mechanism Learning with Mechanism Induced Data, Senior Member Track, AAAI 2015.
• Haifang Li, Wei Chen, Fei Tian, Tao Qin, and Tie-Yan Liu, Generalization Analysis for Game-theoretic Machine Learning, AAAI 2015.
• 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.
• Liang He, Bin Shao, Yatao Li, Enhong Chen. Distributed Real-Time Knowledge Graph Serving. BigComp 2015. Invited Paper.

2014:

• Chang Xu, Yalong Bai, Jiang Bian, Bin Gao, and Tie-Yan Liu, A General Approach to Incorporate Knowledge into Word Representation, CIKM 2014.
• 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.
• Siyu Qiu, Qing Cui, Jiang Bian, Bin Gao, and Tie-Yan Liu, Co-learning of Word Representations and Morpheme Representations, COLING 2014.
• Bin Gao, Jiang Bian, and Tie-Yan Liu, Knowledge Powered Deep Learning for Word Embedding, ECML/PKDD 2014.
• Junpei Komiyama and Tao Qin, Time-Decaying Bandits for Non-stationary Systems, WINE 2014.
• Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, Liwei Wang, Generalized Second Price Auction with Probabilistic Broad Match, EC 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.
• 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.
• Fei Tian, Bin Gao and Tie-Yan Liu, Learning Deep Representations for Graph Clustering, AAAI 2014.
• 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.
• Tie-Yan Liu, Weidong Ma, Tao Qin, and Tao Wu, Generalized Second Price Auctions with Value Externalities, AAMAS 2014.
• Jiang Bian, Taifeng Wang, and Tie-Yan Liu, Sampling Dilemma: Towards Effective Data Sampling for Click Prediction in Sponsored Search, WSDM 2014.
• Lu Wang, Yanghua Xiao, Bin Shao, Haixun Wang, How to Partition a Billion-Node Graph ICDE 2014.
• Huanhuan Xia, Tun Lu, Bin Shao, Guo Li, Xianghua Ding, Ning Gu, A partial Replication Approach for Anywhere Anytime Mobile Commenting   CSCW 2014.
• Zichao Qi, Yanghua Xiao, Bin Shao, Haixun Wang. Toward a Distance Oracle for Billion-Node Graphs. PVLDB 2014.

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.
• Weihao Kong, Jian Li, Tie-Yan Liu and Tao Qin, Optimal Allocation for Chunked-Reward Advertising, WINE 2013.
• Min Xu, Tao Qin, and Tie-Yan Liu, Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising, NIPS 2013.
• 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.
• Bin Shao, Haixun Wang, Yatao Li, Trinity: A Distributed Graph Engine on a Memory Cloud, SIGMOD 2013.  
• Kai Zeng, Jiacheng Yang, Haixun Wang, Bin Shao, Zhongyuan Wang, A Distributed Graph Engine for Web Scale RDF Data, PVLDB 2013.
• Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu, A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search, IJCAI 2013.
• Wenkui Ding, Tao Qin, and Tie-Yan Liu, Multi-Armed Bandit with Budget Constraint and Variable Costs, AAAI 2013.
• Haifeng Xu, Diyi Yang, Bin Gao and Tie-Yan Liu, Predicting Advertiser Bidding Behaviors in Sponsored Search by Rationality Modeling, WWW 2013.

2012:

• Lei Yao, Wei Chen and Tie-Yan Liu, Convergence Analysis for Weighted Joint Strategy Fictitious Play in Generalized Second Price Auction, WINE 2012.
• 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.
• Chenyan Xiong, Taifeng Wang, Wenkui Ding, Yidong Shen, Tie-Yan Liu. Relational Click Prediction for Sponsored Search, WSDM 2012.
• Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Tie-Yan Liu, Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space. NIPS 2012.
• Zhao Sun, Hongzhi Wang, Haixun Wang, Bin Shao, and Jianzhong Li. Efficient Subgraph Matching on Billion Node Graphs. PVLDB 2012

2011:

• Bin Gao, Tie-Yan Liu, Taifeng Wang, Wei Wei, and Hang Li, Semi-supervised graph ranking with rich meta data, KDD 2011
• Zhicong Cheng, Bin Gao, Congkai Sun, Yanbing Jiang, and Tie-Yan Liu. Let Web Spammers Expose Themselves, WSDM 2011.

2010:

• Zhicong Cheng, Bin Gao, and Tie-Yan Liu, Actively Predicting Diverse Search Intent from User Browsing Behaviors, WWW 2010.
• Tao Qin, Xiubo Geng, and Tie-Yan Liu, A New Probabilistic Model for Rank Aggregation, NIPS 2010.
• Wei Chen, Tie-Yan Liu, Zhiming Ma, Two-Layer Generalization Analysis for Ranking Using Rademacher Average, NIPS 2010.
• Jiang Bian, Tie-Yan Liu, Tao Qin, and Hongyuan Zha, Query-dependent Loss Function for Web Search. WSDM 2010.

Pre-2010:

• Fen Xia, Tie-Yan Liu, Hang Li, Statistical Consistency of Top-k Ranking, NIPS 2009.
• Wei Chen, Tie-Yan Liu, Yanyan Lan, Zhiming Ma, Hang Li, Ranking Measures and Loss Functions in Learning to Rank, NIPS 2009.
• Yanyan Lan, Tie-Yan Liu, Zhiming Ma, and Hang Li. Generalization Analysis for Listwise Learning to Rank Algorithms, ICML 2009.
• Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, and Hang Li. Global Ranking Using Continuous Conditional Random Fields, NIPS 2008.
• Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. Listwise Approach to Learning to Rank: Theory and Algorithm, ICML 2008.
• Yanyan Lan, Tie-Yan Liu, Tao Qin, Zhiming Ma, and Hang Li. Query-level Stability and Generalization in Learning to Rank, ICML 2008.
• 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.
• Xiubo Geng, Tie-Yan Liu, Tao Qin, Andrew Arnold, Hang Li, and Heung-Yeung Shum. Query-dependent Ranking using K-Nearest Neighbor, SIGIR 2008.
• 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.
• Jun Xu, Tie-Yan Liu, Min Lu, Hang Li, and Wei-Ying Ma. Directly Optimizing IR Evaluation Measures in Learning to Rank, SIGIR 2008.
• Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. Learning to Rank: From Pairwise Approach to Listwise Approach. ICML 2007.
• Yuting Liu, Tie-Yan Liu, Tao Qin, Zhi-Ming Ma, and Hang Li. Supervised Rank Aggregation, WWW 2007.
• Xiubo Geng, Tie-Yan Liu, Tao Qin, and Hang Li. Feature Selection for Ranking, SIGIR 2007.
• Mingfeng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma. FRank: A Ranking Method with Fidelity Loss, SIGIR 2007.
• Tao Qin, Tie-Yan Liu, Wei Lai, Xu-Dong Zhang, De-Sheng Wang, and Hang Li. Ranking with Multiple Hyperplanes, SIGIR 2007.
• Guang Feng, Tie-Yan Liu, Ying Wang, Ying Bao, Zhiming Ma, Xu-Dong Zhang, and Wei-Ying Ma. AggregateRank: Bringing Order to Websites, SIGIR 2006.
• Yunbo Cao, Jun Xu, Tie-Yan Liu, Hang Li, Yalou Huang, and Hsiao-Wuen Hon. Adapting Ranking SVM to Document Retrieval, SIGIR 2006.
• Qiankun Zhao, Chuhong Hoi, Tie-Yan Liu, Sourav S. Bhowmick, Michael R. Lyu, and Wei-Ying Ma. Time-Dependent Semantic Similarity Measure of Queries Using Historical Click-Through Data, WWW 2006.
• Qiankun Zhao, Tie-Yan Liu, Sourav S. Bhowmick, and Wei-Ying Ma. Event Detection from Evolution of Click-through Data, KDD 2006.
• Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, Zheng Chen, and Wei-Ying Ma. A Study on Relevance Propagation for Web Search, SIGIR 2005.
• 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.
• Bin Gao, Tie-Yan Liu, Tao Qin, Xin Zheng, Qian-Sheng Cheng, and Wei-Ying Ma. Web Image Clustering by Consistent Utilization of Visual Features and Surrounding Texts, ACM Multimedia 2005.
• Tie-Yan Liu, Tao Qin and Hong-Jiang Zhang. Time-constraint Boost for TV Commercials Detection. IEEE ICIP 2004.
• Bin Gao, Tie-Yan Liu, Qian-Sheng Cheng, and Wei-Ying Ma. A Linear Approximation Based Method for Noise-Robust and Illumination-invariant Image Change Detection. PCM 2004.