D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 66 Citations 27,059 355 World Ranking 1077 National Ranking 634

Research.com Recognitions

Awards & Achievements

2017 - IEEE Fellow For contributions to machine learning for web search and online advertising

2016 - ACM Distinguished Member

2012 - ACM Senior Member

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

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.

His most cited work include:

  • Learning to Rank for Information Retrieval (1780 citations)
  • Learning to rank: from pairwise approach to listwise approach (1442 citations)
  • LightGBM: a highly efficient gradient boosting decision tree (1214 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (39.01%)
  • Machine learning (19.80%)
  • Machine translation (14.46%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (39.01%)
  • Speech recognition (8.91%)
  • Speech synthesis (4.95%)

In recent papers he was focusing on the following fields of 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.

Between 2019 and 2021, his most popular works were:

  • Incorporating BERT into Neural Machine Translation (69 citations)
  • FastSpeech 2: Fast and High-Quality End-to-End Text to Speech (49 citations)
  • On Layer Normalization in the Transformer Architecture (36 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Learning to Rank for Information Retrieval

Tie-Yan Liu.
(2009)

3166 Citations

LightGBM: a highly efficient gradient boosting decision tree

Guolin Ke;Qi Meng;Thomas Finley;Taifeng Wang.
neural information processing systems (2017)

2289 Citations

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)

1898 Citations

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)

631 Citations

Listwise approach to learning to rank: theory and algorithm

Fen Xia;Tie-Yan Liu;Jue Wang;Wensheng Zhang.
international conference on machine learning (2008)

624 Citations

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

Tie-Yan Liu;Jun Xu;Tao Qin;Wenying Xiong.
(2007)

498 Citations

Achieving Human Parity on Automatic Chinese to English News Translation

Hany Hassan;Anthony Aue;Chang Chen;Vishal Chowdhary.
arXiv: Computation and Language (2018)

460 Citations

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)

446 Citations

Learning deep representations for graph clustering

Fei Tian;Bin Gao;Qing Cui;Enhong Chen.
national conference on artificial intelligence (2014)

404 Citations

Dual learning for machine translation

Di He;Yingce Xia;Tao Qin;Liwei Wang.
neural information processing systems (2016)

384 Citations

Best Scientists Citing Tie-Yan Liu

Maarten de Rijke

Maarten de Rijke

University of Amsterdam

Publications: 64

Xueqi Cheng

Xueqi Cheng

Chinese Academy of Sciences

Publications: 46

Yi Chang

Yi Chang

Jilin University

Publications: 42

Jiafeng Guo

Jiafeng Guo

Chinese Academy of Sciences

Publications: 38

Craig Macdonald

Craig Macdonald

University of Glasgow

Publications: 37

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 35

Iadh Ounis

Iadh Ounis

University of Glasgow

Publications: 35

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

Publications: 34

Marcos André Gonçalves

Marcos André Gonçalves

Universidade Federal de Minas Gerais

Publications: 34

W. Bruce Croft

W. Bruce Croft

University of Massachusetts Amherst

Publications: 30

Donald Metzler

Donald Metzler

Google (United States)

Publications: 30

Jun Xu

Jun Xu

Renmin University of China

Publications: 30

Hang Li

Hang Li

ByteDance

Publications: 28

Zheng Chen

Zheng Chen

Microsoft (United States)

Publications: 25

Yong Yu

Yong Yu

Shanghai Jiao Tong University

Publications: 24

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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