Portrait of Tie-Yan Liu

Tie-Yan Liu

Assistant Managing Director
微软亚洲研究院 副院长

Recent Papers (chronological)

[Book]

  • Tie-Yan Liu. Learning to Rank for Information Retrieval, Springer, 2011.
  • Tie-Yan Liu, Wei Chen, Taifeng Wang, and Fei Gao, Distributed Machine Learning, Theories, Algorithms, and Systems, China Machine Press, 2018

[Journal Papers]

  • Wenzheng Hu, Junqi Jin, Tie-Yan Liu, and Changshui Zhang, Automatically Design Convolutional Neural Networks by Optimization with Submodularity and Supermodularity, IEEE Transactions on Neural Networks and Learning Systems, 2019.
  • Lijun Wu, Xu Tan, Tao Qin, Jianhuang Lai, Tie-Yan Liu, Beyond Error Propagation: Language Branching Also Affects the Accuracy of Sequence Generation, IEEE Transactions on Audio, Speech and Language Processing, 2019.
  • Li He, Shuxin Zheng, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu, OptQuant: Distributed Training of Neural Networks with Optimized Quantization Mechanisms, NeuroComputing, 2019.
  • Fei Tian, Tao Qin, and Tie-Yan Liu, Computational Pricing in Internet Era, Frontiers of Computer Science, 2018.
  • Liang He, Bin Shao, Yanghua Xiao, Yatao Li, Tie-Yan Liu, Enhong Chen, and Huanhuan Xia, Neurally-Guided Semantic Navigation in Knowledge Graph, IEEE Transactions on Big Data, 2018.
  • 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]

  • Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu, FastSpeech: Fast, Robust and Controllable Text to Speech, NeurIPS 2019.
  • Derek Yang, Li Zhao, Zichuan Lin, Jiang Bian, Tao Qin, and Tie-Yan Liu, Fully Parameterized Quantile Function for Distributional Reinforcement Learning, NeurIPS 2019.
  • Zichuan Lin, Li Zhao,  Derek Yang, Tao Qin, Guangwen Yang, and Tie-Yan Liu, Distributional Reward Decomposition for Reinforcement LearningNeurIPS 2019.
  • Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, ChengXiang Zhai, and Tie-Yan Liu, Neural Machine Translation with Soft Prototype, NeurIPS 2019.
  • Lu Hou, Jinhua Zhu, James Tin-Yau Kwok, Fei Gao, Tao Qin, and Tie-Yan Liu, Normalization Helps Training of Quantized LSTM, NeurIPS 2019.
  • Zhuohan Li, Zi Lin, Di He, Fei Tian, Tao QIN, Liwei WANG and Tie-Yan Liu, Hint-based Training for Non-AutoRegressive Machine Translation, EMNLP 2019
  • Lijun Wu, Jinhua Zhu, Fei Gao, Di He, Tao QIN, Jianhuang Lai and Tie-Yan Liu, Machine Translation With Weakly Paired Documents, EMNLP 2019
  • Xu Tan, Jiale Chen, Di He, Yingce Xia, Tao QIN and Tie-Yan Liu, Multilingual Neural Machine Translation with Language Clustering, EMNLP 2019
  • Lijun Wu, Yiren Wang, Yingce Xia, Tao Qin, Jianwen Lai, and Tie-Yan Liu, Exploiting Monolingual Data at Scale for Neural Machine Translation, EMNLP 2019
  • Jinhua Zhu, Fei Gao, Lijun Wu, Yingce Xia, Tao Qin, Wengang Zhou, Xueqi Cheng, and Tie-Yan Liu, Soft Contextual Data Augmentation for Neural Machine Translation, ACL 2019
  • Yichong Leng, Xu Tan, Tao QIN, Xiang-Yang Li and Tie-Yan Liu, Unsupervised Pivot Translation for Distant Languages, ACL 2019
  • Lijun Wu, Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao QIN and Tie-Yan Liu, Depth Growing for Neural Machine Translation, ACL 2019
  • Chang Xu, Tao Qin, Gang Wang, Tie-Yan Liu, Polygon-Net: A General Framework for Jointly Boosting Multiple Unsupervised Neural Machine Translation Models, IJCAI 2019
  • Mingyang Yi, Huishuai Zhang, Wei Chen, Zhi-Ming Ma , Tie-Yan Liu, BN-invariant Sharpness Regularizes the Training Model to Better Generalization, IJCAI 2019.
  • Zhige Li, Derek Yang, Li Zhao, Jiang Bian, Tao Qin, Tie-Yan Liu, Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding, KDD 2019.
  • Guolin Ke, Zhenhui Xu, Jia Zhang, Jiang Bian and Tie-Yan Liu. DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks, KDD 2019.
  • Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu, MASS: Masked Sequence to Sequence Pre-training for Language Generation, ICML 2019
  • Yi Ren, Xu Tan, Tao Qin, Zhou Zhao, Sheng Zhao, Tie-Yan Liu, Almost Unsupervised Text to Speech and Automatic Speech Recognition, ICML 2019
  • Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, and Tie-Yan Liu, Efficient Training of BERT by Progressively Stacking, ICML 2019
  • Lijun Zhang, Tie-Yan Liu, and Zhi-Hua Zhou, Adaptive Regret of Convex and Smooth Functions, ICML 2019
  • Xihan Li, Jia Zhang, Jiang Bian, Yunhai Tong, and Tie-Yan Liu, A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network, AAMAS 2019.
  • Qi Meng, Shuxin Zheng, Huishuai Zhang, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu, G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space, ICLR 2019
  • Xu Tan, Yi Ren, Di He, Tao Qin, and Tie-Yan Liu, Multilingual Neural Machine Translation with Knowledge Distillation, ICLR 2019
  • Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, and Tie-Yan Liu, Representation Degeneration Problem in Training Natural Language Generation Models, ICLR 2019
  • Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, ChengXiang Zhai, and Tie-Yan Liu, Multi-Agent Dual Learning, ICLR 2019
  • Shuxin Zheng, Qi Meng, Huishuai Zhang, Wei Chen, and Tie-Yan Liu, Capacity Control of ReLU Neural Networks by Basis-path Norm, AAAI 2019
  • Yiren Wang, Fei Tian, Di He, Tao Qin, Chengxiang Zhai, Tie-Yan Liu, Non-Autoregressive Machine Translation with Auxiliary Regularization, AAAI 2019
  • Junliang Guo, Xu Tan, Di He, Tao Qin, Tie-Yan Liu, Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input, AAAI 2019
  • Guoqing Liu, Li Zhao, Feidiao Yang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu, Trust Region Evolution Strategies, AAAI 2019
  • Chang Xu, Weiran Huang, Hongwei Wang, Gang Wang and Tie-Yan Liu, Modeling Local Dependence in Natural Language with Multi-channel Recurrent Neural Networks, AAAI 2019
  • Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu, Improving Word Embedding by Adversarial Training, NIPS 2018.
  • Lijun Wu, Fei Tian, Yingce Xia, Tao Qin, Tie-Yan Liu, Learning to Teach with Dynamic Loss Functions, NIPS 2018.
  • Renqian Luo, Fei Tian, Tao Qin, Tie-Yan Liu, Automatic Neural Architecture Design: From Search to Optimization, NIPS 2018.
  • Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, Tie-Yan Liu, Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation, NIPS 2018.
  • Huishuai Zhang, Wei Chen, and Tie-Yan Liu, On the Local Hessian in Back-propagation, NIPS 2018.
  • Xu Tan, Lijun Wu, Di He, Fei Tian, Tao QIN, Jianhuang Lai and Tie-Yan Liu, Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter, EMNLP 2018.
  • Lijun Wu, Fei Tian, Tao Qin, Jianhuang Lai and Tie-Yan Liu, A Study of Reinforcement Learning for Neural Machine Translation, EMNLP 2018.
  • Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Model-Level Dual Learning, ICML 2018.
  • Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, and Tie-Yan Liu, Towards Binary-Valued Gates for Robust LSTM Training, ICML 2018.
  • Kaitao Song, Xu Tan, Di He, Jianfeng Lu, Tao Qin, and Tie-Yan Liu, Double Path Networks for Sequence to Sequence Learning, COLING 2018.
  • Hany Hassan, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt, William Lewis, Mu Li, Shujie Liu, Tie-Yan Liu, Renqian Luo, Arul Menezes, Tao Qin, Frank Seide, Xu Tan, Fei Tian, Lijun Wu, Shuangzhi Wu, Yingce Xia, Dongdong Zhang, Zhirui Zhang, Ming Zhou, Achieving Human Parity on Automatic Chinese to English News Translation, arXiv 2018.
  • Chenyan Xiong, Zhengzhong Liu, Jamie Callan and Tie-Yan Liu, Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling, SIGIR 2018.
  • Li Han, Qi Meng, Wei Chen, Zhiming Ma, Tie-Yan Liu, Differential Equations for Modeling Asynchronous Algorithms, IJCAI 2018.
  • Fei Tian, Tao Qin, and Tie-Yan Liu, Learning to Teach, ICLR 2018.
  • Jianxin Lin, Yingce Xia, Tao Qin, Zhibo Chen, and Tie-Yan Liu, Conditional Image-to-Image Translation, CVPR 2018.
  • Fei Gao, Lijun Wu, Li Zhao, Tao Qin, and Tie-Yan Liu, Efficient Sequence Learning with Group Recurrent Networks, NAACL 2018.
  • Yanyao Shen, Xu Tan, Di He, Tao Qin, and Tie-Yan Liu, Dense Information Flow for Neural Machine Translation, NAACL 2018.
  • Shizhao Sun, Wei Chen, Jiang Bian, Tie-Yan Liu, Slim-DP: A Multi-Agent System for Communication-Efficient Distributed Deep Learning, AAMAS 2018.
  • Yijun Wang , Yingce Xia , Li Zhao , Jiang Bian , Tao Qin, Guiquan Liu , Tie-Yan Liu, Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization, AAAI 2018.
  • Lijun Wu, Fei Tian , Li Zhao , JianHuang Lai , Tie-Yan Liu, Word Attention for Sequence to Sequence Text Understanding, AAAI 2018.
  • Ziniu Hu, Weiqing Liu, Jiang Bian, Xuanzhe Liu, Tie-Yan Liu, Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction, WSDM 2018.
  • Yingce  Xia ,  Lijun  Wu ,  Jianxin  Lin ,  Fei  Tian ,  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, 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, ICML 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.
  • Quanming Yao, James Kwok, Fei Gao, Wei Chen, and Tie-Yan Liu, Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems, IJCAI 2017.
  • Chenyan Xiong, Jamie Callan, and Tie-Yan Liu, Learning to Attend and to Rank with Word-Entity Duets, SIGIR 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, AAAI 2017.
  • Shizhao Sun, Wei Chen, Jiang Bian, and Tie-Yan Liu, Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks, ECML 2017.
  • Fei Tian, Yingce Xia, Tao Qin, Tie-Yan Liu, Sequence Generation with Target Attention, ECML 2017.
  • Xiang Li, Tao Qin, and Tie-Yan Liu, LightRNN: Compuation and Memory Efficient Recurrent Neural Networks, NIPS 2016
  • Di He, Yingce Xia, Tao Qin, Tie-Yan Liu, and Wei-Ying Ma, Dual Learning for Machine Translation, 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.
  • 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.
  • 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.
  • Shizhao Sun, Wei Chen, Liwei Wang, and Tie-Yan Liu, On the Depth of Deep Neural Networks: A Theoretical View, AAAI 2016.
  • 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.
  • 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.
  • 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.
  • 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.
  • Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Tie-Yan Liu, Kwok-Tung Lo, Xu-Dong Zhang, and Jian Feng. Constant False-alarm Ratio Processing for Video Cut Detection, IEEE ICIP 2002.
  • Jian Feng, Tie-Yan Liu, Kwok-Tung Lo , and Xu-Dong Zhang. Adaptive Motion Tracking for Fast Block Motion Estimation, IEEE ISCAS 2001.