H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 31 Citations 6,869 122 World Ranking 7730 National Ranking 3613

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Ji Liu spends much of his time researching Algorithm, Asynchronous communication, Speedup, Stochastic gradient descent and Coordinate descent. His Algorithm research incorporates themes from Stochastic approximation, Saddle, Acceleration, Convex optimization and Pattern recognition. His Convex optimization study incorporates themes from Artificial neural network, Artificial intelligence, Bounded function and Shared memory.

His work deals with themes such as Event, Machine learning and Data mining, which intersect with Artificial intelligence. In his study, Support vector machine, Stochastic optimization, Convolutional neural network and Computation is inextricably linked to Bottleneck, which falls within the broad field of Stochastic gradient descent. His Coordinate descent study improves the overall literature in Mathematical optimization.

His most cited work include:

  • Tensor completion for estimating missing values in visual data (1038 citations)
  • Sparse reconstruction cost for abnormal event detection (552 citations)
  • Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent (365 citations)

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

His scientific interests lie mostly in Artificial intelligence, Algorithm, Mathematical optimization, Machine learning and Reinforcement learning. His research ties Pattern recognition and Artificial intelligence together. When carried out as part of a general Algorithm research project, his work on Coordinate descent is frequently linked to work in Asynchronous communication, therefore connecting diverse disciplines of study.

The various areas that he examines in his Mathematical optimization study include Regularization, Bounded function and Proximal Gradient Methods. Ji Liu has included themes like Stochastic optimization and Variance reduction in his Reinforcement learning study. His study in Rate of convergence is interdisciplinary in nature, drawing from both Stochastic gradient descent and Server.

He most often published in these fields:

  • Artificial intelligence (36.89%)
  • Algorithm (21.36%)
  • Mathematical optimization (20.39%)

What were the highlights of his more recent work (between 2020-2021)?

  • Artificial intelligence (36.89%)
  • Regret (4.37%)
  • Key (6.31%)

In recent papers he was focusing on the following fields of study:

His main research concerns Artificial intelligence, Regret, Key, Bottleneck and Scalability. Artificial intelligence is frequently linked to Function in his study. His Function research is multidisciplinary, incorporating elements of Stochastic gradient descent, Variance reduction and Reinforcement learning.

His Regret research includes elements of World Wide Web, Social network, Monte Carlo tree search and Federated learning. His studies examine the connections between Bottleneck and genetics, as well as such issues in Data mining, with regards to Embedding. His work focuses on many connections between Scalability and other disciplines, such as System model, that overlap with his field of interest in Feature.

Between 2020 and 2021, his most popular works were:

  • Central Server Free Federated Learning over Single-sided Trust Social Networks (19 citations)
  • Towards Statistically Provable Geometric 3D Human Pose Recovery (2 citations)
  • Streaming Probabilistic Deep Tensor Factorization (1 citations)

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.

Top Publications

Tensor completion for estimating missing values in visual data

Ji Liu;Przemyslaw Musialski;Peter Wonka;Jieping Ye.
international conference on computer vision (2009)

1242 Citations

Sparse reconstruction cost for abnormal event detection

Yang Cong;Junsong Yuan;Ji Liu.
computer vision and pattern recognition (2011)

737 Citations

Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent

Xiangru Lian;Ce Zhang;Huan Zhang;Cho-Jui Hsieh.
neural information processing systems (2017)

382 Citations

Abnormal event detection in crowded scenes using sparse representation

Yang Cong;Junsong Yuan;Ji Liu.
Pattern Recognition (2013)

289 Citations

Gradient Sparsification for Communication-Efficient Distributed Optimization

Jianqiao Wangni;Jialei Wang;Ji Liu;Tong Zhang.
neural information processing systems (2018)

262 Citations

Asynchronous parallel stochastic gradient for nonconvex optimization

Xiangru Lian;Yijun Huang;Yuncheng Li;Ji Liu.
neural information processing systems (2015)

261 Citations

Asynchronous Stochastic Coordinate Descent: Parallelism and Convergence Properties

Ji Liu;Stephen J. Wright.
Siam Journal on Optimization (2015)

226 Citations

D$^2$: Decentralized Training over Decentralized Data

Hanlin Tang;Xiangru Lian;Ming Yan;Ce Zhang.
arXiv: Distributed, Parallel, and Cluster Computing (2018)

216 Citations

An asynchronous parallel stochastic coordinate descent algorithm

Ji Liu;Stephen J. Wright;Christopher Ré;Victor Bittorf.
Journal of Machine Learning Research (2015)

212 Citations

Asynchronous Decentralized Parallel Stochastic Gradient Descent

Xiangru Lian;Wei Zhang;Ce Zhang;Ji Liu.
international conference on machine learning (2018)

205 Citations

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

If you think any of the details on this page are incorrect, let us know.

Contact us

Top Scientists Citing Ji Liu

Peter Richtárik

Peter Richtárik

King Abdullah University of Science and Technology

Publications: 63

Wotao Yin

Wotao Yin

Alibaba Group (China)

Publications: 42

Ce Zhang

Ce Zhang

ETH Zurich

Publications: 41

Mingyi Hong

Mingyi Hong

University of Minnesota

Publications: 37

Heng Huang

Heng Huang

University of Pittsburgh

Publications: 35

Ting-Zhu Huang

Ting-Zhu Huang

University of Electronic Science and Technology of China

Publications: 29

Andrzej Cichocki

Andrzej Cichocki

Skolkovo Institute of Science and Technology

Publications: 29

Qibin Zhao

Qibin Zhao

RIKEN

Publications: 28

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 27

Michael K. Ng

Michael K. Ng

University of Hong Kong

Publications: 27

Soummya Kar

Soummya Kar

Carnegie Mellon University

Publications: 23

Zhangyang Wang

Zhangyang Wang

The University of Texas at Austin

Publications: 23

H. Vincent Poor

H. Vincent Poor

Princeton University

Publications: 23

Yu Zhang

Yu Zhang

Southern University of Science and Technology

Publications: 22

Christopher Ré

Christopher Ré

Stanford University

Publications: 21

Something went wrong. Please try again later.