Tie-Yan Liu

Distinguished Scientist,
Microsoft Research AI for Science

[AI for Science]

Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu, Predicting equilibrium distributions for molecular systems with deep learning, Nature Machine Intelligence, 2024.
He Zhang, Chang Liu, Zun Wang, Xinran Wei, Siyuan Liu, Nanning Zheng, Bin Shao, Tie-Yan Liu, Self-Consistency Training for Hamiltonian Prediction, ICML 2024.
Xinquan Huang, Wenlei Shi, Xiaotian Gao, Xinran Wei, Jia Zhang, Jiang Bian, Mao Yang, Tie-Yan Liu, LordNet: An efficient neural network for learning to solve parametric partial differential equations without simulated data, Neural Networks, 2024.
Congqiao Li, Huilin Qu, Sitian Qian, Qi Meng, Shiqi Gong, Jue Zhang, Tie-Yan Liu, Qiang Li, Does Lorentz-symmetric design boost network performance in jet physics? Physical Review D, 2024.
Tong Wang, Yusong Wang, Shaoning Li, Xinheng He, Mingyu Li, Zun Wang, Nanning Zheng, Bin Shao, and Tie-Yan Liu, Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing, Nature Communications, 2023 (Editor’s highlights).
Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Veličković, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio, and Marinka Zitnik, Scientific Discovery in the Age of Artificial Intelligence, Nature, 2023.
Tong Wang, Xinheng He, Mingyu Li, Bin Shao, Tie-Yan Liu, AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics, Scientific Data, 2023.
Zun Wang, Hongfei Wu, Lixin Sun, Xinheng He, Zhirong Liu, Bin Shao, Tong Wang, Tie-Yan Liu, Improving machine learning force fields for molecular dynamics simulations with fine-grained force metrics, The Journal of Chemical Physics, 2023.
Yusong Wang, Shaoning Li, Tong Wang, Bin Shao, Nanning Zheng, Tie-Yan Liu, Geometric Transformer with Interatomic Positional Encoding, NeurIPS 2023.
Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan, FABind: Fast and Accurate Protein-Ligand Binding, NeurIPS 2023.
Jinhua Zhu, Yingce Xia, Lijun Wu, Shufang Xie, Wengang Zhou, Tao Qin, Houqiang Li, Tie-Yan Liu, Dual-view Molecular Pre-training, KDD 2023.
Hangting Ye, Zhining Liu, Wei Cao, Amir Mohammad Amiri, Jiang Bian, Yi Chang, Jon D. Lurie, Jim Weinstein, Tie-Yan Liu, Web-based Long-term Spine Treatment Outcome Forecasting, KDD 2023.
Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Tianbo Peng, Yingce Xia, Liang He, Shufang Xie, Tao Qin, Haiguang Liu, Kun He, Tie-Yan Liu, Pre-training Antibody Language Models for Antigen-Specific Computational Antibody Design. KDD 2023.
Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu, Retrosynthetic Planning with Dual Value Networks, ICML 2023.
Xinquan Huang, Wenlei Shi, Qi Meng, Yue Wang, Xiaotian Gao, Jia Zhang, Tie-Yan Liu, NeuralStagger: accelerating physics-constrained neural PDE solver with spatial-temporal decomposition, ICML 2023.
Zequn Liu, Wei Zhang, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Ming Zhang and Tie-Yan Liu, MT-GPT: Wrapping Molecules with Text for Generative Pre-training, ACL 2023.
Zijie Geng, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Jie Wang, Yongdong Zhang, Feng Wu, Tie-Yan Liu, De Novo Molecular Generation via Connection-aware Motif Mining, ICLR 2023.
Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He, One Transformer Can Understand Both 2D & 3D Molecular Data, ICLR 2023.
Jinhua Zhu, Kehan Wu, Bohan Wang, Yingce Xia, Shufang Xie, Qi Meng, Lijun Wu, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu, O-GNN: incorporating ring priors into molecular modeling, ICLR 2023.
Jinhua Zhu, Yue Wang, Lijun Wu, Tao Qin, Wengang Zhou, Tie-Yan Liu, Houqiang Li, Making Better Decision by Directly Planning in Continuous Control, ICLR 2023.
Di He, Wenlei Shi, Shanda Li, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, Tie-Yan Liu, Learning Physics-Informed Neural Networks without Stacked Back-propagation, AISTATS 2023.
Shiqi Gong, Qi Meng, Yue Wang, Lijun Wu, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu, Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics, NeuroComputing, 2023.
Shiqi Gong, Yue Wang, Qi Meng, Ni Hao, Tie-Yan Liu, Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations, AAAI 2023.
Shiqi Gong, Xinheng He, Qi Meng, Zhiming Ma, Bin Shao, Tong Wang, Tie-Yan Liu, Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics, Journal of Physical Chemistry, 2022 (Cover page article).
Rui Zhang, Peiyan Hu, Qi Meng, Yue Wang, Rongchan Zhu, Bingguang Chen, Zhi-Ming Ma, and Tie-Yan Liu, DRVN (Deep Random Vortex Network): A New Physics-informed Machine Learning Method for Simulating and Inferring Incompressible Fluid Flows, Physics of Fluids, 2022.
Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Tong Wang, Yusong Wang, Wengang Zhou, Tao Qin, Houqiang Li, Tie-Yan Liu, Direct Molecular Conformation Generation, Transaction on Machine Learning Research, 2022.
Yutai Hou, Yingce Xia, Lijun Wu, Shufang Xie, Yang Fan, Jinhua Zhu, Wanxiang Che, Tao Qin, Tie-Yan Liu, Discovering Drug-Target Interaction Knowledge from Biomedical Literature, Bioinformatics, 2022.
Lijun Wu, Chengcan Yin, Jinhua Zhu, Zhen Wu, Liang He, Yingce Xia, Shufang Xie, Tao Qin, Tie-Yan Liu. SPRoBERTa: Protein Embedding Learning with Local Fragment Modeling. Briefings in Bioinformatics, 2022.
Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, Tie-Yan Liu, BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining. Briefings in Bioinformatics, 2022
Shiqi Gong, Qi Meng, Jue Zhang, Huilin Qu, Congqiao Li, Sitian Qian, Weitao Du, Zhi-Ming Ma, Tie-Yan Liu, An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging, Journal of High Energy Physics, 2022.
Jinhua Zhu, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu, Unified 2D and 3D Pre-Training of Molecular Representations, KDD 2022.
Siyuan Liu, Yusong Wang, Yifan Deng, Liang He, Bin Shao, Jian Yin, Nanning Zheng, Tie-Yan Liu, Tong Wang, Improved drug–target interaction prediction with intermolecular graph transformer, Briefings in Bioinformatics, 2022.
Jia Xing, Siwei Li, Shuxin Zheng, Chang Liu, Xiaochun Wang, Lin Huang, Ge Song, Yihan He, Shuxiao Wang, Shovan Kumar Sahu, Jia Zhang, Jiang Bian, Yun Zhu, Tie-Yan Liu, Jiming Hao. Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder. Environmental Science & Technology, 2022.
Xinquan Wang, Jun Lan, Xinheng He, Yifei Ren, Ziyi Wang, Huan Zhou, Shilong Fan, Chenyou Zhu, Dongsheng Liu, Bin Shao, Tie-Yan Liu, Qisheng Wang, Linqi Zhang, Jiwan Ge, and Tong Wang, Structural insights into the SARS-CoV-2 Omicron RBD-ACE2 interaction, Cell Research 2022.
Estee Y Cramer, Evan L Ray, Velma K Lopez, Johannes Bracher, Andrea Brennen, Alvaro J Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie H House, Yuxin Huang, Dasuni Jayawardena, Abdul H Kanji, Ayush Khandelwal, Khoa Le, Anja Mühlemann, Jarad Niemi, Apurv Shah, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W Zorn, Youyang Gu, Sansiddh Jain, Nayana Bannur, Ayush Deva, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Neil F Abernethy, Spencer Woody, Maytal Dahan, Spencer Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G Scott, Mauricio Tec, Ajitesh Srivastava, Glover E George, Jeffrey C Cegan, Ian D Dettwiller, William P England, Matthew W Farthing, Robert H Hunter, Brandon Lafferty, Igor Linkov, Michael L Mayo, Matthew D Parno, Michael A Rowland, Benjamin D Trump, Yanli Zhang-James, Samuel Chen, Stephen V Faraone, Jonathan Hess, Christopher P Morley, Asif Salekin, Dongliang Wang, Sabrina M Corsetti, Thomas M Baer, Marisa C Eisenberg, Karl Falb, Yitao Huang, Emily T Martin, Ella McCauley, Robert L Myers, Tom Schwarz, Daniel Sheldon, Graham Casey Gibson, Rose Yu, Liyao Gao, Yian Ma, Dongxia Wu, Xifeng Yan, Xiaoyong Jin, Yu-Xiang Wang, YangQuan Chen, Lihong Guo, Yanting Zhao, Quanquan Gu, Jinghui Chen, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Hannah Biegel, Joceline Lega, Steve McConnell, V P Nagraj, Stephanie L Guertin, Christopher Hulme-Lowe, Stephen D Turner, Yunfeng Shi, Xuegang Ban, Robert Walraven, Qi-Jun Hong, Stanley Kong, Axel van de Walle, James A Turtle, Michal Ben-Nun, Steven Riley, Pete Riley, Ugur Koyluoglu, David DesRoches, Pedro Forli, Bruce Hamory, Christina Kyriakides, Helen Leis, John Milliken, Michael Moloney, James Morgan, Ninad Nirgudkar, Gokce Ozcan, Noah Piwonka, Matt Ravi, Chris Schrader, Elizabeth Shakhnovich, Daniel Siegel, Ryan Spatz, Chris Stiefeling, Barrie Wilkinson, Alexander Wong, Sean Cavany, Guido España, Sean Moore, Rachel Oidtman, Alex Perkins, David Kraus, Andrea Kraus, Zhifeng Gao, Jiang Bian, Wei Cao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Alessandro Vespignani, Matteo Chinazzi, Jessica T Davis, Kunpeng Mu, Ana Pastore Y Piontti, Xinyue Xiong, Andrew Zheng, Jackie Baek, Vivek Farias, Andreea Georgescu, Retsef Levi, Deeksha Sinha, Joshua Wilde, Georgia Perakis, Mohammed Amine Bennouna, David Nze-Ndong, Divya Singhvi, Ioannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas, Arnab Sarker, Ali Jadbabaie, Devavrat Shah, Nicolas Della Penna, Leo A Celi, Saketh Sundar, Russ Wolfinger, Dave Osthus, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dean Karlen, Matt Kinsey, Luke C Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Elizabeth C Lee, Juan Dent, Kyra H Grantz, Alison L Hill, Joshua Kaminsky, Kathryn Kaminsky, Lindsay T Keegan, Stephen A Lauer, Joseph C Lemaitre, Justin Lessler, Hannah R Meredith, Javier Perez-Saez, Sam Shah, Claire P Smith, Shaun A Truelove, Josh Wills, Maximilian Marshall, Lauren Gardner, Kristen Nixon, John C Burant, Lily Wang, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Yueying Wang, Shan Yu, Robert C Reiner, Ryan Barber, Emmanuela Gakidou, Simon I Hay, Steve Lim, Chris Murray, David Pigott, Heidi L Gurung, Prasith Baccam, Steven A Stage, Bradley T Suchoski, B Aditya Prakash, Bijaya Adhikari, Jiaming Cui, Alexander Rodríguez, Anika Tabassum, Jiajia Xie, Pinar Keskinocak, John Asplund, Arden Baxter, Buse Eylul Oruc, Nicoleta Serban, Sercan O Arik, Mike Dusenberry, Arkady Epshteyn, Elli Kanal, Long T Le, Chun-Liang Li, Tomas Pfister, Dario Sava, Rajarishi Sinha, Thomas Tsai, Nate Yoder, Jinsung Yoon, Leyou Zhang, Sam Abbott, Nikos I Bosse, Sebastian Funk, Joel Hellewell, Sophie R Meakin, Katharine Sherratt, Mingyuan Zhou, Rahi Kalantari, Teresa K Yamana, Sen Pei, Jeffrey Shaman, Michael L Li, Dimitris Bertsimas, Omar Skali Lami, Saksham Soni, Hamza Tazi Bouardi, Turgay Ayer, Madeline Adee, Jagpreet Chhatwal, Ozden O Dalgic, Mary A Ladd, Benjamin P Linas, Peter Mueller, Jade Xiao, Yuanjia Wang, Qinxia Wang, Shanghong Xie, Donglin Zeng, Alden Green, Jacob Bien, Logan Brooks, Addison J Hu, Maria Jahja, Daniel McDonald, Balasubramanian Narasimhan, Collin Politsch, Samyak Rajanala, Aaron Rumack, Noah Simon, Ryan J Tibshirani, Rob Tibshirani, Valerie Ventura, Larry Wasserman, Eamon B O’Dea, John M Drake, Robert Pagano, Quoc T Tran, Lam Si Tung Ho, Huong Huynh, Jo W Walker, Rachel B Slayton, Michael A Johansson, Matthew Biggerstaff, Nicholas G Reich, Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States, PNAS, 2022.
Alan Junji Yamaguchi, Toru Sato, Takaomi Tobase, Xinran Wei, Lin Huang, Jia Zhang, Jiang Bian, Tie-Yan Liu. Development of a Permeability Reduction Model using Deep Learning for CO2 Hydrate Storage. International Conference on Ocean, Offshore and Arctic Engineering, 2022.
Jia Xing, Shuxin Zheng, Siwei Li, Lin Huang, Xiaochun Wang, James T. Kelly, Shuxiao Wang, Chang Liu, Carey Jang, Yun Zhu, Jia Zhang, Jiang Bian, Tie-Yan Liu, Jiming Hao, Mimicking Atmospheric Photochemical Modeling with a Deep Neural Network, Atmospheric Research, 2021.,
Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, and Tie-Yan Liu, Machine-learning Nonconservative Dynamics for New Physics Detection, Physical Review E, 2021.
He Zhang, Fusong Ju, Jianwei Zhu, Liang He, Bin Shao, Nanning Zheng, Tie-Yan Liu, Co-evolution Transformer for Protein Contact Prediction, NeurIPS 2021.
Yao Li, Tong Wang, Juanrong Zhang, Bin Shao, Haipeng Gong, Yusong Wang, Xinheng He, Siyuan Liu and Tie-Yan Liu, Exploring the Regulatory Function of the N-terminal Domain of SARS-CoV-2 Spike Protein Through Molecular Dynamics Simulation, Advanced Theory and Simulation, 2021 (Cover page article).
Huang, Lin and Liu, Song and Yang, Zeyuan and Xing, Jia and Zhang, Jia and Bian, Jiang and Li, Siwei and Kumar Sahu, Shovan and Wang, Shuxiao and Liu, Tie-Yan. Exploring deep learning for air pollutant emission estimation. Geoscientific Model Development, 2021, 14(7): 4641-4654.
Siyuan Liu, Qijiang Xu, Tong Wang, Bin Shao, Jian Yin, and Tie-Yan Liu, Complementing Sequence-derived Features with Structural Information Extracted from Fragment Libraries for Protein Structure Prediction, BMC Bioinformatics, 2021
Wenze Ding, Qijiang Xu, Siyuan Liu, Tong Wang, Bin Shao, Haipeng Gong and Tie-Yan Liu. SAMF: a self-adaptive protein modeling framework. Bioinformatics, 2021.
Fusong Ju, Jianwei Zhu, Bin Shao, Lupeng Kong, Tie-Yan Liu, Wei-Mou Zheng, and Dongbo Bu, CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction, Nature Communications, 2021.
Jia Xing, Shuxin Zheng, Dian Ding, James T. Kelly, Shuxiao Wang, Siwei Li, Tao Qin, Mingyuan Ma, Zhaoxin Dong, Carey Jang, Yun Zhu, Haotian Zheng, Lu Ren, Tie-Yan Liu, and Jiming Hao, Deep Learning for Prediction of the Air Quality Response to Emission Changes, Environmental Science & Technology, 2020

[Learning to Rank and Information Retrieval]

Tie-Yan Liu. Learning to Rank for Information Retrieval, Springer, 2011 [Book]
Chenyan Xiong, Zhengzhong Liu, Jamie Callan and Tie-Yan Liu, Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling, SIGIR 2018
Chenyan Xiong, Jamie Callan, and Tie-Yan Liu, Learning to Attend and to Rank with Word-Entity Duets, SIGIR 2017
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
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
Shanshan Huang, Shuaiqiang Wang, Tie-Yan Liu, Jun Ma, Zhumin Chen, and Jari Veijalainen, Listwise Collaborative Filtering, SIGIR 2015
Bin Gao, Tie-Yan Liu, Taifeng Wang, Wei Wei, and Hang Li, Semi-supervised graph ranking with rich meta data, KDD 2011
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
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
Jiang Bian, Tie-Yan Liu, Tao Qin, and Hongyuan Zha, Query-dependent Loss Function for Web Search. WSDM 2010
Wei Chen, Tie-Yan Liu, Yanyan Lan, Zhiming Ma, Hang Li, Ranking Measures and Loss Functions in Learning to Rank, NIPS 2009
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, 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
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 (Best student paper).
Jun Xu, Tie-Yan Liu, Min Lu, Hang Li, and Wei-Ying Ma. Directly Optimizing IR Evaluation Measures in Learning to Rank, SIGIR 2008
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
Ying Bao, Guang Feng, Tie-Yan Liu, Zhiming Ma and Ying Wang. Ranking Websites: A Probabilistic View, Internet Mathematics, 2007
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
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
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
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
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
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
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

[Computational Advertising and Mechanism Design]

Fei Tian, Tao Qin, and Tie-Yan Liu, Computational Pricing in Internet Era, Frontiers of Computer Science, 2018
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
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
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
Wei Chen, Tie-Yan Liu, and Xinxin Yang, Reinforcement Learning Behaviors in Sponsored Search, Applied Stochastic Models in Business and Industry, 2016
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
Binyi Chen, Tao Qin, and Tie-Yan Liu, Mechanism Design for Daily Deals, AAMAS 2015
Tie-Yan Liu, Wei Chen, and Tao Qin, Mechanism Learning with Mechanism Induced Data, Senior Member Track, AAAI 2015
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
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
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
Weihao Kong, Jian Li, Tie-Yan Liu and Tao Qin, Optimal Allocation for Chunked-Reward Advertising, WINE 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
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

[Dual Learning]

Jinpeng Li, Yingce Xia, Hongda Sun, Dongyan Zhao, Tie-Yan Liu, Rui Yan, Stylized Dialogue Generation with Multi-Pass Dual Learning, NeurIPS 2021.
Yi Ren, Xu Tan, Tao Qin, Zhou Zhao, Sheng Zhao, Tie-Yan Liu, Almost Unsupervised Text to Speech and Automatic Speech Recognition, ICML 2019
Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, ChengXiang Zhai, and Tie-Yan Liu, Multi-Agent Dual Learning, ICLR 2019
Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Model-Level Dual Learning, ICML 2018
Jianxin Lin, Yingce Xia, Tao Qin, Zhibo Chen, and Tie-Yan Liu, Conditional Image-to-Image Translation, CVPR 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
Yingce Xia, Tao Qin, Wei Chen, Tie-Yan Liu, Dual Supervised Learning, ICML 2017
Yingce Xia, Jiang Bian, Tao Qin, Tie-Yan Liu, Dual Inference for Machine Learning, IJCAI 2017
Di He, Yingce Xia, Tao Qin, Tie-Yan Liu, and Wei-Ying Ma, Dual Learning for Machine Translation, NIPS 2016

[Distributed Machine Learning / Lightweight Machine Learning]

Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu, Quantized Training of Gradient Boosted Decision Trees, NeurIPS 2022.
Tie-Yan Liu, Wei Chen, Taifeng Wang, and Fei Gao, Distributed Machine Learning, Theories, Algorithms, and Systems, China Machine Press, 2018 [Book]
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
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
Li Han, Qi Meng, Wei Chen, Zhiming Ma, Tie-Yan Liu, Differential Equations for Modeling Asynchronous Algorithms, IJCAI 2018
Shizhao Sun, Wei Chen, Jiang Bian, Tie-Yan Liu, Slim-DP: A Multi-Agent System for Communication-Efficient Distributed Deep Learning, AAMAS 2018
Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, and Tie-Yan Liu, Asynchronous Stochastic Gradient Descent with Delay Compensation, ICML 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
Shizhao Sun, Wei Chen, Jiang Bian, and Tie-Yan Liu, Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks, ECML 2017
Quanming Yao, James Kwok, Fei Gao, Wei Chen, and Tie-Yan Liu, Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems, IJCAI 2017
Guolin Ke, Qi Meng, Taifeng Wang, Wei Chen, Weidong Ma, Tie-Yan Liu, LightGBM: A Highly Efficient Gradient Boosting Decision Tree, NIPS 2017
Xiang Li, Tao Qin, and Tie-Yan Liu, LightRNN: Compuation and Memory Efficient Recurrent Neural Networks, 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
Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang and Tie-Yan Liu, Asynchronous Accelerated Stochastic Gradient Descent, IJCAI 2016
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

[Sequence Learning: Language and Speech]

Botao Yu, Peiling Lu, Rui Wang, Wei Hu, Xu Tan, Wei Ye, Shikun Zhang, Tao Qin, Tie-Yan Liu, Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation, NeurIPS 2022.
Yichong Leng, Zehua Chen, Junliang Guo, Haohe Liu, Jiawei Chen, Xu Tan, Danilo Mandic, Lei He, Xiangyang Li, Tao Qin, Sheng Zhao, Tie-Yan Liu, BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis, NeurIPS 2022.
Yi Ren, Xu Tan, Tao Qin, Zhou Zhao, Tie-Yan Liu, Revisiting Over-Smoothness in Text to Speech, ACL 2022.
Chang Liu, Xu Tan, Chongyang Tao, Zhenxin Fu, Dongyan Zhao, Tie-Yan Liu, Rui Yan, ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation, ACL 2022.
Shufang Xie, Ang Lv, Yingce Xia, Lijun Wu, Tao Qin, Tie-Yan Liu, Rui Yan, Target-Side Data Augmentation for Sequence Generation, ICLR 2022.
Jiawei Chen, Xu Tan, Yichong Leng, Jin Xu, Guihua Wen, Tao Qin, Tie-Yan Liu, Speech-T: Transducer for Text to Speech and Beyond, NeurIPS 2021.
Yichong Leng, Xu Tan, Linchen Zhu, Jin Xu, Renqian Luo, Linquan Liu, Tao Qin, Xiangyang Li, Edward Lin, Tie-Yan Liu, FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition, NeurIPS 2021
Yuzi Yan, Xu Tan, Bohan Li, Guangyan Zhang, Tao Qin, Sheng Zhao, Yuan Shen, Wei-Qiang Zhang and Tie-Yan Liu, AdaSpeech 3: Adaptive Text to Speech for Spontaneous Style, InterSpeech 2021.
Lanqing Xue, Kaitao Song, Duocai Wu, Xu Tan, Nevin L. Zhang, Tao Qin, Wei-Qiang Zhang, and Tie-Yan Liu. DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling. ACL 2021 (Long paper).
Yuzi Yan, Xu Tan, Bohan Li, Tao Qin, Sheng Zhao, Yuan Shen, Tie-Yan Liu, UAdaSpeech: Adaptive Text to Speech with Untranscribed Data, ICASSP 2021.
Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Jinzhu Li, Sheng Zhao, Enhong Chen, Tie-Yan Liu, LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search, ICASSP 2021.
Chen Zhang, Yi Ren, Xu Tan, Jinglin Liu, Kejun Zhang, Tao Qin, Sheng Zhao, Tie-Yan Liu, DenoiSpeech: Denoising Text to Speech with Frame-Level Noise Modeling, ICASSP 2021.
Guolin Ke, Di He, Tie-Yan Liu, Re-thinking Positional Encoding in Language Pretraining, ICLR 2021
Qiyu Wu, Chen Xing, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu, Taking Notes on the Fly Helps Language Pretraining, ICLR 2021
Mingjian Chen, Xu Tan, Bohan Li, Yanqing Liu, Tao Qin, sheng zhao, Tie-Yan Liu, AdaSpeech: Adaptive Text to Speech for Custom Voice, ICLR 2021
Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu, FastSpeech 2: Fast and High-Quality End-to-End Text to Speech, ICLR 2021
Chen Zhang, Xu Tan, Yi Ren, Tao Qin, Kejun Zhang, Tie-Yan Liu, UWSpeech: Speech to Speech Translation for Unwritten Languages, AAAI 2021
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu, MPNet: Masked and Permuted Pre-training for Language Understanding, NeurIPS 2020
Mingjian Chen, Xu Tan, Yi Ren, Jin Xu, Hao Sun, Sheng Zhao, Tao QIN and Tie-Yan Liu, MultiSpeech: Multi-Speaker Text to Speech with Transformer, InterSpeech 2020
Lijun Wu, Shufang Xie, Yingce Xia, Yang Fan, Jian-Huang Lai, Tao Qin, Tie-Yan Liu, Sequence Generation with Mixed Representations, ICML 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
Jinglin Liu, Yi Ren, Xu Tan, Chen Zhang, Tao Qin, Zhou Zhao, Tie-Yan Liu, Task-Level Curriculum Learning for Non-Autoregressive Neural Machine Translation, IJCAI 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
Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, and Tie-Yan Liu, SEEK: Segmented Embedding of Knowledge Graph, ACL 2020
Yi Ren, Jinglin Liu, Xu Tan, Zhou Zhao, Sheng Zhao and Tie-Yan Liu, A Study of Non-autoregressive Model for Sequence Generation, ACL 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
Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, ChengXiang Zhai, and Tie-Yan Liu, Neural Machine Translation with Soft Prototype, NeurIPS 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
Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, ChengXiang Zhai, and Tie-Yan Liu, Neural Machine Translation with Soft Prototype, 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
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu, MASS: Masked Sequence to Sequence Pre-training for Language Generation, 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
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, 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
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
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
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
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, Tao Qin, Jianhuang Lai and Tie-Yan Liu, A Study of Reinforcement Learning for Neural Machine Translation, EMNLP 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 (opens in new tab), arXiv 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
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
Lijun Wu, Fei Tian , Li Zhao , JianHuang Lai , Tie-Yan Liu, Word Attention for Sequence to Sequence Text Understanding, AAAI 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
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
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
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

[Reinforcement Learning and Bandits]

Yuanying Cai, Chuheng Zhang, Li Zhao, Wei Shen, Xuyun Zhang, Lei Song, Jiang Bian, Tao Qin, Tie-Yan Liu, TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets, ICDM 2022 (Best student paper runner-up).
Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu, Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret, NeurIPS 2022.
Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng, Pushi Zhang, Li Zhao, Wenxue Cheng, Peng CHENG, Yongqiang Xiong, Tao Qin, Jianyu Chen, Tie-Yan Liu, An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context, NeurIPS 2022.
Guoqing Liu, Mengzhang Cai, Li Zhao, Tao Qin, Adrian Brown, Jimmy Bischoff and Tie-Yan Liu, Inspector: Pixel-based Automated Game Testing via Exploration, Detection, and Investigation, IEEE CoG 2022.
Yue Jin, Yue Zhang, Tao Qin, Xudong Zhang, Jian Yuan, Houqiang Li, Tie-Yan Liu, Supervised Off-Policy Ranking, ICML 2022.
Jinhua Zhu, Yingce Xia, Lijun Wu, Jiajun Deng, Wengang Zhou, Tao Qin, Tie-Yan Liu, and Houqiang Li, Masked Contrastive Representation Learning for Reinforcement Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
Chongchong Li, Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, Tie-Yan Liu, Gradient Information Matters in Policy Optimization by Back-propagating through Model, ICLR 2022.
Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu, Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality, ICLR 2022.
Pushi Zhang, Xiaoyu Chen, Li Zhao, Wei Xiong, Tao Qin, Tie-Yan Liu, Distributional Reinforcement Learning for Multi-Dimensional Reward Functions, NeurIPS 2021.
Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu, Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning, NeurIPS 2021.
Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu, Curriculum Offline Imitating Learning, NeurIPS 2021.
Pushi Zhang, Li Zhao, Guoqing Liu, Jiang Bian, Minlie Huang, Tao Qin, Tie-Yan Liu, Independence-aware Advantage Estimation, IJCAI 2021.
Tianhao Zhang, Qiwei Ye, Jiang Bian, Guangming Xie, Tie-Yan Liu, MFVFD: Mean-Field based Individual Value Function Decomposition Multi-Agent Q-Learning for Stochastic Games, IJCAI 2021.
Guoqing Liu, Chuheng Zhang, Li Zhao, Tao Qin, Jinhua Zhu, Li Jian, Nenghai Yu, Tie-Yan Liu, Return-Based Contrastive Representation Learning for Reinforcement Learning, ICLR 2021
Wenlei Shi, Xinran Wei, Jia Zhang, Xiaoyuan Ni, Arthur Jiang, Jiang Bian and Tie-Yan Liu, Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representation, AAMAS 2021.
Zichuan Lin, Derek Yang, Li Zhao, Tao Qin, Guangwen Yang, Tie-Yan Liu, RD^2: Reward Decomposition with Representation Decomposition, NeurIPS 2020
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
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
Guoqing Liu, Li Zhao, Feidiao Yang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu, Trust Region Evolution Strategies, AAAI 2019
Yue Wang, Wei Chen, Yuting Liu, and Tie-Yan Liu, Finite Sample Analysis of GTD Policy Evaluation Algorithms in Markov Setting, NIPS 2017
Lijun Wu, Li Zhao, Tao Qin, and Tie-Yan Liu, Sequence Prediction with Unlabeled Data by Reward Function Learning, IJCAI 2017
Yingce Xia, Tao Qin, Weidong Ma, Nenghai Yu and Tie-Yan Liu, Budgeted Multi-armed Bandits with Multiple Plays, IJCAI 2016
Yingce Xia, Tao Qin, Tie-Yan Liu, Best Action Selection in a Stochastic Environment, AAMAS 2016
Yingce Xia, Haifang Li, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Thompson Sampling for Budgeted Multi-armed Bandits, IJCAI 2015
Wenkui Ding, Tao Qin, and Tie-Yan Liu, Multi-Armed Bandit with Budget Constraint and Variable Costs, AAAI 2013
Min Xu, Tao Qin, and Tie-Yan Liu, Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising, NIPS 2013
Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu, Online Learning for Auction Mechanism in Bandit Setting, Decision Support Systems, 2013

[Deep Learning]

Xu Tan, Tao Qin, Jiang Bian, Tie-Yan Liu, Yoshua Bengio, Regeneration learning: A learning paradigm for data generation, AAAI 2024.
Juntao Li, Xiaobo Liang, Lijun Wu, Yue Wang, Qi Meng, Tao Qin, Min Zhang, Tie-Yan Liu, Randomness Regularization with Simple Consistency Training for Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He, Your Transformer May Not be as Powerful as You Expect, NeurIPS 2022.
Bohan Wang, Qi Meng, Huishuai Zhang, Ruoyu Sun, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu, Does Momentum Change the Implicit Regularization on Separable Data? NeurIPS 2022.
Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Tie-Yan Liu, Nanning Zheng, Bin Shao, Equivariant graph neural networks with complete local frames, ICML 2022.
Jin Xu, Xu Tan, Kaitao Song, Renqian Luo, Yichong Leng, Tao Qin, Tie-Yan Liu, Jian Li, Analyzing and Mitigating Interference in Neural Architecture Search, ICML 2022.
Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu, Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart, CVPR 2022.
Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu, DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting, ICLR 2022.
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu, Do Transformers Really Perform Bad for Graph Representation? NeurIPS 2021.
Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu, Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding, NeurIPS 2021.
Xiaobo Liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, Tie-Yan Liu, R-Drop: Regularized Dropout for Neural Networks, NeurIPS 2021.
Jin Xu, Xu Tan, Renqian Luo, Kaitao Song, Jian Li, Tao Qin, Tie-Yan Liu, NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search, KDD 2021.
Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang, GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training, ICML 2021.
Xueqing Wu, Lewen Wang, Yingce Xia, Weiqing Liu, Lijun Wu, Shufang Xie, Tao Qin, and Tie-Yan Liu, Temporally Correlated Task Scheduling for Sequence Learning, ICML 2021.
Xufang Luo, Qi Meng, Wei Chen, Yunhong Wang, and Tie-Yan Liu, Path-BN: Towards Effective Batch Normalization in the Path Space for ReLU Networks, UAI 2021.
Jinhua Zhu, Lijun Wu, Yingce Xia, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu, IOT: Instance-wise Layer Reordering for Transformer Structures, ICLR 2021
Da Yu, Huishuai Zhang, Wei Chen, Tie-Yan Liu, Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning, ICLR 2021
Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, Tie-Yan Liu, Semi-Supervised Neural Architecture Search, NeurIPS 2020
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tie-Yan Liu, On Layer Normalization in the Transformer Architecture, ICML 2020
Yang Fan, Fei Tian, Yingce Xia, Tao Qin, Xiangyang Li, and Tie-Yan Liu, Searching Better Architectures for Neural Machine Translation, IEEE Transactions on Audio, Speech and Language Processing, 2020
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
Lu Hou, Jinhua Zhu, James Tin-Yau Kwok, Fei Gao, Tao Qin, and Tie-Yan Liu, Normalization Helps Training of Quantized LSTM, NeurIPS 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
Feidiao Yang, Tiancheng Jin, Tie-Yan Liu, Xiaoming Sun, Jialin Zhang, Boosting Dynamic Programming with Neural Networks for Solving NP-hard Problems, ACML 2018 (Best student paper).
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
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, Neural Architecture Optimization, NIPS 2018
Fei Tian, Tao Qin, and Tie-Yan Liu, Learning to Teach, ICLR 2018
Fei Tian, Yingce Xia, Tao Qin, Tie-Yan Liu, Sequence Generation with Target Attention, ECML 2017
Fei Tian, Bin Gao and Tie-Yan Liu, Learning Deep Representations for Graph Clustering, AAAI 2014

[Machine Learning Theory]

Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu, What Makes Your Data Unavailable To Deep Learning? KDD 2022.
Bohan Wang, Huishuai Zhang, Jieyu Zhang, Qi Meng, Wei Chen, Tie-Yan Liu, Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD, NeurIPS 2021.
Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu, PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior, ICLR 2022.
Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu, On the Generative Utility of Cyclic Conditionals, NeurIPS 2021.
Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu, Recovering Latent Causal Factor for Generalization to Distributional Shifts, NeurIPS 2021.
Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu, Learning Causal Semantic Representation for Out-of-Distribution Prediction, NeurIPS 2021.
Bohan Wang, Qi Meng, Wei Chen, Tie-Yan Liu, The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks, ICML 2021.
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu, Large Scale Private Learning via Low-rank Reparametrization, ICML 2021.
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu, How Does Data Augmentation Affect Privacy in Machine Learning? AAAI 2021
Shicong Cen, Huishuai Zhang, Yuejie Chi, Wei Chen, Tie-Yan Liu, Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data, IEEE Transactions on Signal Processing, 2020
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu, Gradient Perturbation is Underrated for Differentially Private Convex Optimization, IJCAI 2020
Mingyang Yi, Huishuai Zhang, Wei Chen, Zhi-Ming Ma , Tie-Yan Liu, BN-invariant Sharpness Regularizes the Training Model to Better Generalization, IJCAI 2019
Lijun Zhang, Tie-Yan Liu, and Zhi-Hua Zhou, Adaptive Regret of Convex and Smooth Functions, ICML 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
Shuxin Zheng, Qi Meng, Huishuai Zhang, Wei Chen, and Tie-Yan Liu, Capacity Control of ReLU Neural Networks by Basis-path Norm, AAAI 2019
Huishuai Zhang, Wei Chen, and Tie-Yan Liu, On the Local Hessian in Back-propagation, NIPS 2018
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
Shizhao Sun, Wei Chen, Liwei Wang, and Tie-Yan Liu, On the Depth of Deep Neural Networks: A Theoretical View, AAAI 2016
Haifang Li, Wei Chen, Fei Tian, Tao Qin, and Tie-Yan Liu, Generalization Analysis for Game-theoretic Machine Learning, AAAI 2015
Fei Tian, Haifang Li, Wei Chen, Tao Qin and Tie-Yan Liu, Agent Behavior Prediction and Its Generalization Analysis, AAAI 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
Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Tie-Yan Liu, Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space. NIPS 2012
Wei Chen, Tie-Yan Liu, Zhiming Ma, Two-Layer Generalization Analysis for Ranking Using Rademacher Average, NIPS 2010
Fen Xia, Tie-Yan Liu, Hang Li, Statistical Consistency of Top-k Ranking, NIPS 2009
Yanyan Lan, Tie-Yan Liu, Zhiming Ma, and Hang Li. Generalization Analysis for Listwise Learning to Rank Algorithms, ICML 2009
Yanyan Lan, Tie-Yan Liu, Tao Qin, Zhiming Ma, and Hang Li. Query-level Stability and Generalization in Learning to Rank, ICML 2008

[AI for Industry]

Dinglan Peng, Shuxin Zheng, Yatao Liu, Guolin Ke, Di He, and Tie-Yan Liu, Code Representation with Operational Semantics for Abstract Machine, ICML 2021.
Wentao Xu, Chang Xu, Weiqing Liu, Jiang Bian, and Tie-Yan Liu, REST: Relational Event-driven Stock Trend Forecasting, WebConf 2021.
Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, Tie-Yan Liu, Universal Trading for Order Execution with Oracle Policy Distillation, AAAI 2021
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
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
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

[Multimedia/Computer Vision]

Weicong Chen, Xu Tan, Yingce Xia, Tao Qin, Yu Wang, and Tie-Yan Liu, DualLip: A System for Joint Lip Reading and Generation. ACM Multimedia 2020
Yi Ren, Jinzheng He, Xu Tan, Tao Qin, Zhou Zhao, and Tie-Yan Liu, PopMAG: Pop Music Accompaniment Generation. ACM Multimedia 2020
Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu, Invertible Image Rescaling, ECCV 2020
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, 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 (Most cited paper).
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
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

Tie-Yan Liu

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Contact Tie-Yan Liu

Microsoft Research AI for Science