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D-Index & Metrics

Computer Science

D-Index
91
Citations
49762
World Ranking
565
National Ranking
301

Research.com Recognitions

  • 2017 - IEEE Fellow For contributions to machine learning for web search and online advertising
  • 2016 - ACM Distinguished Member
  • 2012 - ACM Senior Member

Overview

Tie-Yan Liu is affiliated with Microsoft in the United States and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence. Their research spans multiple subfields, including artificial intelligence, molecular biology, computer vision and pattern recognition, signal processing, and computational theory and mathematics.

The main topics of their work include natural language processing techniques, topic modeling, speech recognition and synthesis, computational drug discovery methods, protein structure and dynamics, machine learning in materials science, and music and audio processing.

Tie-Yan Liu has published numerous papers in various venues, with a substantial number appearing on arXiv (Cornell University). Other notable publication venues include the Proceedings of the AAAI Conference on Artificial Intelligence, Briefings in Bioinformatics, bioRxiv (Cold Spring Harbor Laboratory), and the IEEE Transactions on Pattern Analysis and Machine Intelligence.

Some of the recent papers by Tie-Yan Liu are:

  • Scientific discovery in the age of artificial intelligence (2023), Nature
  • BioGPT: generative pre-trained transformer for biomedical text generation and mining (2022), Briefings in Bioinformatics
  • R-Drop: Regularized Dropout for Neural Networks (2021), arXiv (Cornell University)
  • A Survey on Neural Speech Synthesis (2021), arXiv (Cornell University)
  • Incorporating BERT into Neural Machine Translation (2020), arXiv (Cornell University)

Collaborations have been a significant aspect of their work, with frequent co-authors including Tao Qin, Yingce Xia, Tong Wang, Shufang Xie, and Lijun Wu.

Tie-Yan Liu has been recognized through several professional distinctions. Notably, they were named an IEEE Fellow in 2017 for contributions to machine learning for web search and online advertising. They were also designated an ACM Distinguished Member in 2016 and an ACM Senior Member in 2012.

Best Publications

  • LightGBM: a highly efficient gradient boosting decision tree

    Guolin Ke;Qi Meng;Thomas Finley;Taifeng Wang

  • Learning to Rank for Information Retrieval

    Tie-Yan Liu

  • Learning to rank: from pairwise approach to listwise approach

    Zhe Cao;Tao Qin;Tie-Yan Liu;Ming-Feng Tsai

  • BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining

    Unknown

  • MASS: Masked Sequence to Sequence Pre-training for Language Generation

    Kaitao Song;Xu Tan;Tao Qin;Jianfeng Lu

  • Listwise approach to learning to rank: theory and algorithm

    Fen Xia;Tie-Yan Liu;Jue Wang;Wensheng Zhang

  • Dual learning for machine translation

    Di He;Yingce Xia;Tao Qin;Liwei Wang

  • FastSpeech: Fast, Robust and Controllable Text to Speech

    Yi Ren;Yangjun Ruan;Xu Tan;Tao Qin

  • Adapting ranking SVM to document retrieval

    Yunbo Cao;Jun Xu;Tie-Yan Liu;Hang Li

  • Learning deep representations for graph clustering

    Fei Tian;Bin Gao;Qing Cui;Enhong Chen

  • Achieving Human Parity on Automatic Chinese to English News Translation

    Hany Hassan;Anthony Aue;Chang Chen;Vishal Chowdhary

  • FastSpeech 2: Fast and High-Quality End-to-End Text to Speech

    Yi Ren;Chenxu Hu;Xu Tan;Tao Qin

  • LETOR: A benchmark collection for research on learning to rank for information retrieval

    Tao Qin;Tie-Yan Liu;Jun Xu;Hang Li

  • MPNet: Masked and Permuted Pre-training for Language Understanding

    Kaitao Song;Xu Tan;Tao Qin;Jianfeng Lu

  • Neural Architecture Optimization

    Renqian Luo;Fei Tian;Tao Qin;Enhong Chen

  • LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval

    Tie-Yan Liu;Jun Xu;Tao Qin;Wenying Xiong

  • Do Transformers Really Perform Badly for Graph Representation

    Chengxuan Ying;Tianle Cai;Shengjie Luo;Shuxin Zheng

  • On Layer Normalization in the Transformer Architecture

    Ruibin Xiong;Yunchang Yang;Di He;Kai Zheng

  • Dual Learning for Machine Translation

    Yingce Xia;Di He;Tao Qin;Liwei Wang

  • Sequential click prediction for sponsored search with recurrent neural networks

    Yuyu Zhang;Hanjun Dai;Chang Xu;Jun Feng

  • Introducing LETOR 4.0 Datasets.

    Tao Qin;Tie-Yan Liu

  • A Theoretical Analysis of NDCG Type Ranking Measures

    Yining Wang;Liwei Wang;Yuanzhi Li;Di He

  • A Highly Efficient Gradient Boosting Decision Tree

    Guolin Ke;Qi Meng;Taifeng Wang;Wei Chen

Frequent Co-Authors

Tao Qin
Tao Qin Microsoft (United States)
Hang Li
Hang Li ByteDance
Wei-Ying Ma
Wei-Ying Ma Tsinghua University
Liwei Wang
Liwei Wang Peking University
Zhou Zhao
Zhou Zhao Zhejiang University
Enhong Chen
Enhong Chen University of Science and Technology of China
ChengXiang Zhai
ChengXiang Zhai University of Illinois at Urbana-Champaign
Jun Xu
Jun Xu Renmin University of China
Xueqi Cheng
Xueqi Cheng Chinese Academy of Sciences
James T. Kwok
James T. Kwok Hong Kong University of Science and Technology

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