Publications
Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, and Tie-Yan Liu. Neural Architecture Search with GBDT. arXiv 2020.
Junjie Li, Sotetsu Koyamada, Qiwei Ye, Guoqing Liu, Chao Wang, Ruihan Yang, Li Zhao, Tao Qin, Tie-Yan Liu, and Hsiao-Wuen Hon. Suphx: Mastering Mahjong with Deep Reinforcement Learning. arXiv 2020.
Jin Xu, Xu Tan, Yi Ren, Tao Qin, Jian Li, Sheng Zhao, and Tie-Yan Liu. LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition. KDD 2020.
Yi Ren, Xu Tan, Tao Qin, Jian Luan, Zhou Zhao, and Tie-Yan Liu. DeepSinger: Singing Voice Synthesis with Data Mined From the Web. KDD 2020.
Yang Fan, Fei Tian, Yingce Xia, Tao Qin, Xiangyang Li, and Tie-Yan Liu. Searching Better Architectures for Neural Machine Translation. IEEE/ACM Transactions on Audio, Speech and Language Processing 2020.
Yi Ren, Jinglin Liu, Xu Tan, Chen Zhang, Tao QIN, Zhou Zhao and Tie-Yan Liu. SimulSpeech: End-to-End Simultaneous Speech to Text Translation. ACL 2020.
Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, Tie-Yan Liu, Semi-Supervised Neural Architecture Search, arxiv 2020. [code]
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. MPNet: Masked and Permuted Pre-training for Language Understanding. arXiv 2020.
Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu, Incorporating BERT into Neural Machine Translation, ICLR 2020.
Yiren Wang, Lijun Wu, Yingce Xia, Tao Qin, Cheng Xiang Zhai, Tie-Yan Liu, Transductive Ensemble Learning for Neural Machine Translation, AAAI 2020.
Junliang Guo, Xu Tan, Linli Xu, Tao Qin, Tie-Yan Liu, Enhong Chen, Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation, AAAI 2020.
Yiping Lu, Zhuohan Li, Di He, Zhiqing Sun, Bin Dong, Tao Qin, Liwei Wang, and Tie-Yan Liu, Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View, arxiv 2019.
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 Learning, NeurIPS 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. [code]
Lu Hou, Jinhua Zhu, James Tin-Yau Kwok, Fei Gao, Tao Qin, and Tie-Yan Liu, Normalization Helps Training of Quantized LSTM, NeurIPS 2019. [code]
Hao Sun, Xu Tan, Jun-Wei Gan, Sheng Zhao, Dongxu Han, Hongzhi Liu, Tao Qin, and Tie-Yan Liu, Knowledge Distillation from BERT in Pre-training and Fine-tuning for Polyphone Disambiguation, ASRU 2019.
Hao Sun, Xu Tan, Jun-Wei Gan, Hongzhi Liu, Sheng Zhao, Tao Qin, and Tie-Yan Liu, Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion, InterSpeech 2019.
Lijun Wu, Xu Tan, Tao Qin, Jianhuang Lai and Tie-Yan Liu, Beyond Error Propagation: Language Branching Also Affects the Accuracy of Sequence Generation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019.
Jianxin Lin, Zhibo Chen, Yingce Xia, Sen Liu, Tao Qin, and Jiebo Luo. Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
Yijun Wang, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Enhong Chen, and Tie-Yan Liu, Semi-Supervised Neural Machine Translation via Marginal Distribution Estimation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 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.
Tao Shen, Xiubo Geng, Tao QIN, Daya Guo, Duyu Tang, Nan Duan, Guodong Long, and Daxin Jiang, Multi-task Learning for Conversational Question Answering Over a Large-Scale Knowledge Base. EMNLP 2019.
Yingce Xia, Xu Tan, et al. Microsoft Research Asia’s Systems for WMT19, the fourth Conference on Machine Translation.
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.,
Jianxin Lin, Yingce Xia, Tao Qin, Yijun Wang, Zhibo Chen, Image-to-Image Translation with Multi-Path Consistency Regularization, IJCAI 2019.
Tianyu He, Yingce Xia, Jianxin Lin, Xu Tan, Di He, Tao Qin, Zhibo Chen, Deliberation Learning for Image-to-Image Translation, IJCAI 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.
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.
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu, MASS: Masked Sequence to Sequence Pre-training for Language Generation, ICML 2019. [code]
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. [code]
Jiang Rong, Tao Qin, Bo An, Competitive Bridge Bidding with Deep Neural Networks, AAMAS 2019.
Yibo Sun, Duyu Tang, Nan Duan, Tao Qin, Shujie Liu, Zhao Yan, Ming Zhou, Yuanhua Lv, Wenpeng Yin, Xiaocheng Feng, Bing Qin, Ting Liu, Joint Learning of Question Answering and Question Generation, TKDE 2019.
Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, ChengXiang Zhai, Tie-Yan Liu, Multi-Agent Dual Learning, ICLR 2019.
Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, Tieyan Liu，Representation Degeneration Problem in Training Natural Language Generation Models, ICLR 2019.
Xu Tan, Yi Ren, Di He, Tao Qin, Tie-Yan Liu, Multilingual Neural Machine Translation with Knowledge Distillation, ICLR 2019.
Guoqing Liu, Li Zhao, Feidiao Yang, Jiang Bian, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Trust Region Evolution Strategies, 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, and Tie-Yan Liu, Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input, AAAI 2019.
Yingce Xia, Tianyu He, Xu Tan, Fei Tian, Di He, and Tao Qin, Tied Transformers: Neural Machine Translation with Shared Encoder and Decoder, AAAI 2019.
Chengyue Gong, Xu Tan, Di He, and Tao Qin, Sentence-wise Smooth Regularization for Sequence to Sequence Learning, AAAI 2019.
Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, and Tie-Yan Liu, FRAGE: Frequency-Agnostic Word Representation, NIPS 2018. [code]
Lijun Wu, Fei Tian, Yingce Xia, Tao Qin, Jianhuang Lai, and Tie-Yan Liu, Learning to Teach with Dynamic Loss Functions, NIPS 2018.
Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu, Neural Architecture Optimization, NIPS 2018. [code]
Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, and Tie-Yan Liu, Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation, NIPS 2018.
Lijun Wu, Fei Tian, Tao Qin, Jianhuang Lai and Tie-Yan Liu, A Study of Reinforcement Learning for Neural Machine Translation, EMNLP 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.
Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Model-Level Dual Learning, ICML 2018.
Lijun Wu, Yingce Xia, Li Zhao, Fei Tian, Tao Qin, Jianhuang Lai, and Tie-Yan Liu, Adversarial Neural Machine Translation, ACML 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. [code] [Chinese article]
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, et al. Achieving Human Parity on Automatic Chinese to English News Translation, arXiv 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. [code]
Yang Fan, Fei Tian, Tao Qin, Xiangyang Li, and Tie-Yan Liu, Learning to Teach, ICLR 2018. [Chinese article]
Yijun Wang, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Guiquan Liu, and Tie-Yan Liu, Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization, AAAI 2018.
Jiang Rong, Tao Qin, and Bo An, Dynamic Pricing for Reusable Resources in Competitive Market with Stochastic Demand, AAAI 2018.
Duyu Tang, Nan Duan, Tao Qin, Zhao Yan, and Ming Zhou, Question Answering and Question Generation as Dual Tasks, arXiv 2017.
Chang Xu, Tao Qin, Gang Wang, and Tie-Yan Liu, Reinforcement Learning for Learning Rate Control, arXiv 2017.
Aadharsh Kannan, Justin Rao, Preston McAfee, Di He, Tao Qin and Tie-Yan Liu, Scale Effects in Web Search, WINE 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.
Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu and Tie-Yan Liu, Dual Supervised Learning, 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.
Xujin Chen, Xiaodong Hu, Tie-Yan Liu, Weidong Ma, Tao Qin, Pingzhong Tang, Changjun Wang, Bo Zheng, Efficient Mechanism Design for Online Scheduling (Extended Abstract), 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.
Yingce Xia, Fei Tian, Tao Qin, Nenghai Yu and Tie-Yan Liu, Sequence Generation with Target Attention, ECML 2017.
Xiang Li, Tao Qin, Jian Yang, and Tie-Yan Liu, LightRNN: Memory and Computation-Efficient Recurrent Neural Networks, NIPS 2016. [Code@GitHub]
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: Benchmark Dataset for Research on 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.