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Author Information
Zichuan Lin (Tsinghua University)
Derek Yang (UC San Diego)
Li Zhao (Microsoft Research)
Tao Qin (Microsoft Research)
Guangwen Yang (Tsinghua University)
Tie-Yan Liu (Microsoft Research Asia)
Tie-Yan Liu is an assistant managing director of Microsoft Research Asia, leading the machine learning research area. He is very well known for his pioneer work on learning to rank and computational advertising, and his recent research interests include deep learning, reinforcement learning, and distributed machine learning. Many of his technologies have been transferred to Microsoft’s products and online services (such as Bing, Microsoft Advertising, Windows, Xbox, and Azure), and open-sourced through Microsoft Cognitive Toolkit (CNTK), Microsoft Distributed Machine Learning Toolkit (DMTK), and Microsoft Graph Engine. He has also been actively contributing to academic communities. He is an adjunct/honorary professor at Carnegie Mellon University (CMU), University of Nottingham, and several other universities in China. He has published 200+ papers in refereed conferences and journals, with over 17000 citations. He has won quite a few awards, including the best student paper award at SIGIR (2008), the most cited paper award at Journal of Visual Communications and Image Representation (2004-2006), the research break-through award (2012) and research-team-of-the-year award (2017) at Microsoft Research, and Top-10 Springer Computer Science books by Chinese authors (2015), and the most cited Chinese researcher by Elsevier (2017). He has been invited to serve as general chair, program committee chair, local chair, or area chair for a dozen of top conferences including SIGIR, WWW, KDD, ICML, NIPS, IJCAI, AAAI, ACL, ICTIR, as well as associate editor of ACM Transactions on Information Systems, ACM Transactions on the Web, and Neurocomputing. Tie-Yan Liu is a fellow of the IEEE, and a distinguished member of the ACM.
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2020 Poster: Semi-Supervised Neural Architecture Search »
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2020 Poster: Model-based Adversarial Meta-Reinforcement Learning »
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2020 Poster: MPNet: Masked and Permuted Pre-training for Language Understanding »
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2019 Poster: Neural Machine Translation with Soft Prototype »
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2019 Poster: FastSpeech: Fast, Robust and Controllable Text to Speech »
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2019 Poster: Fully Parameterized Quantile Function for Distributional Reinforcement Learning »
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2018 Poster: Neural Architecture Optimization »
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2018 Poster: Learning to Teach with Dynamic Loss Functions »
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2018 Poster: Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation »
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2018 Poster: FRAGE: Frequency-Agnostic Word Representation »
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2017 Poster: Decoding with Value Networks for Neural Machine Translation »
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2017 Poster: Finite sample analysis of the GTD Policy Evaluation Algorithms in Markov Setting »
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2017 Poster: Deliberation Networks: Sequence Generation Beyond One-Pass Decoding »
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2017 Poster: LightGBM: A Highly Efficient Gradient Boosting Decision Tree »
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2016 Poster: A Communication-Efficient Parallel Algorithm for Decision Tree »
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2016 Poster: Dual Learning for Machine Translation »
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2016 Poster: LightRNN: Memory and Computation-Efficient Recurrent Neural Networks »
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2013 Poster: Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising »
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2012 Poster: Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space »
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2012 Spotlight: Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space »
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2010 Workshop: Machine Learning in Online Advertising »
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2010 Poster: Two-Layer Generalization Analysis for Ranking Using Rademacher Average »
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2010 Poster: A New Probabilistic Model for Rank Aggregation »
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2009 Poster: Statistical Consistency of Top-k Ranking »
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2009 Poster: Ranking Measures and Loss Functions in Learning to Rank »
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2008 Poster: Global Ranking Using Continuous Conditional Random Fields »
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2008 Oral: Global Ranking Using Continuous Conditional Random Fields »
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