2017 - IEEE Fellow For contributions to machine learning for web search and online advertising
2016 - ACM Distinguished Member
2012 - ACM Senior Member
Tie-Yan Liu focuses on Artificial intelligence, Machine learning, Learning to rank, Ranking and Information retrieval. As part of his studies on Artificial intelligence, Tie-Yan Liu often connects relevant subjects like Natural language processing. The various areas that Tie-Yan Liu examines in his Machine learning study include Event, Probabilistic logic and Data mining.
His Learning to rank research is multidisciplinary, relying on both Semi-supervised learning, Pairwise comparison, Ranking SVM and Benchmark. His Ranking research focuses on subjects like Function, which are linked to Basis, Hessian matrix, Compensation and Stochastic gradient descent. His work on Relevance as part of general Information retrieval study is frequently linked to Information storage, bridging the gap between disciplines.
Tie-Yan Liu spends much of his time researching Artificial intelligence, Machine learning, Machine translation, Information retrieval and Data mining. Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Natural language processing. His Machine learning study combines topics in areas such as Training set and Benchmark.
Tie-Yan Liu has researched Machine translation in several fields, including Sentence, Speech recognition, Translation and Automatic summarization. His research integrates issues of Web page and World Wide Web in his study of Information retrieval. Ranking and Ranking are the subject areas of his Learning to rank study.
Artificial intelligence, Speech recognition, Speech synthesis, Machine translation and Inference are his primary areas of study. His Artificial intelligence research includes themes of Machine learning and Natural language processing. The concepts of his Machine learning study are interwoven with issues in Contextual image classification, Training set and Benchmark.
His study in Speech recognition is interdisciplinary in nature, drawing from both Encoder and Context model. His work carried out in the field of Machine translation brings together such families of science as Automatic summarization, Sequence, Natural language understanding and Transformer. His Inference research incorporates elements of Software deployment and Autoregressive model.
His scientific interests lie mostly in Speech recognition, Artificial intelligence, Transformer, Speech synthesis and Machine translation. His Artificial intelligence research integrates issues from Machine learning and Natural language processing. His Machine learning study combines topics from a wide range of disciplines, such as Network architecture and Benchmark.
His studies deal with areas such as Normalization, Control theory, Embedding, Encoder and Initialization as well as Transformer. His research in Speech synthesis intersects with topics in Quality, Inference and Autoregressive model. Tie-Yan Liu combines subjects such as Langevin dynamics, Algorithm and Generative modeling with his study of Leverage.
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LightGBM: a highly efficient gradient boosting decision tree
Guolin Ke;Qi Meng;Thomas Finley;Taifeng Wang.
neural information processing systems (2017)
Learning to Rank for Information Retrieval
Learning to rank: from pairwise approach to listwise approach
Zhe Cao;Tao Qin;Tie-Yan Liu;Ming-Feng Tsai.
international conference on machine learning (2007)
Dual learning for machine translation
Di He;Yingce Xia;Tao Qin;Liwei Wang.
neural information processing systems (2016)
Listwise approach to learning to rank: theory and algorithm
Fen Xia;Tie-Yan Liu;Jue Wang;Wensheng Zhang.
international conference on machine learning (2008)
Adapting ranking SVM to document retrieval
Yunbo Cao;Jun Xu;Tie-Yan Liu;Hang Li.
international acm sigir conference on research and development in information retrieval (2006)
Achieving Human Parity on Automatic Chinese to English News Translation
Hany Hassan;Anthony Aue;Chang Chen;Vishal Chowdhary.
arXiv: Computation and Language (2018)
MASS: Masked Sequence to Sequence Pre-training for Language Generation
Kaitao Song;Xu Tan;Tao Qin;Jianfeng Lu.
international conference on machine learning (2019)
LETOR: A benchmark collection for research on learning to rank for information retrieval
Tao Qin;Tie-Yan Liu;Jun Xu;Hang Li.
Information Retrieval (2010)
Learning deep representations for graph clustering
Fei Tian;Bin Gao;Qing Cui;Enhong Chen.
national conference on artificial intelligence (2014)
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