H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 38 Citations 6,285 137 World Ranking 4972 National Ranking 462

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Peilin Zhao mainly focuses on Artificial intelligence, Machine learning, Online machine learning, Data mining and Algorithm. His Artificial intelligence study frequently links to related topics such as Pattern recognition. Transfer of learning, Training set and Feature is closely connected to Key in his research, which is encompassed under the umbrella topic of Machine learning.

In Data mining, Peilin Zhao works on issues like Statistical classification, which are connected to Binary classification. His research investigates the connection between Feature learning and topics such as Representation that intersect with issues in Deep learning. His work focuses on many connections between Feature extraction and other disciplines, such as Codebook, that overlap with his field of interest in Contextual image classification.

His most cited work include:

  • Local features are not lonely – Laplacian sparse coding for image classification (413 citations)
  • Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life (255 citations)
  • Stochastic Optimization with Importance Sampling for Regularized Loss Minimization (181 citations)

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

Peilin Zhao mostly deals with Artificial intelligence, Machine learning, Online machine learning, Data mining and Mathematical optimization. His Artificial intelligence study incorporates themes from Algorithm design and Pattern recognition. His work on Active learning, Semi-supervised learning and Regret is typically connected to Multi-task learning as part of general Machine learning study, connecting several disciplines of science.

His Data mining research is multidisciplinary, incorporating elements of Margin, Statistical classification and Maximization. His Mathematical optimization study integrates concerns from other disciplines, such as Computational complexity theory, Sampling and Estimator. His work deals with themes such as Continuous-time stochastic process and Stochastic optimization, which intersect with Sampling.

He most often published in these fields:

  • Artificial intelligence (65.00%)
  • Machine learning (55.00%)
  • Online machine learning (13.89%)

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

  • Artificial intelligence (65.00%)
  • Machine learning (55.00%)
  • Deep learning (6.11%)

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

Artificial intelligence, Machine learning, Deep learning, Architecture and Theoretical computer science are his primary areas of study. While working in this field, Peilin Zhao studies both Artificial intelligence and Field. His study connects Cover and Machine learning.

The concepts of his Theoretical computer science study are interwoven with issues in Social media, Recurrent neural network and Directed graph. In his study, Mathematical optimization is strongly linked to Range, which falls under the umbrella field of Pareto principle. His Supervised learning research includes themes of Unsupervised learning and Categorization.

Between 2019 and 2021, his most popular works were:

  • FedML: A Research Library and Benchmark for Federated Machine Learning. (31 citations)
  • Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis (24 citations)
  • Towards Fast Adaptation of Neural Architectures with Meta Learning (24 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

Peilin Zhao focuses on Artificial intelligence, Machine learning, Deep learning, Recurrent neural network and Theoretical computer science. His work on Reinforcement learning and Control as part of general Artificial intelligence study is frequently linked to Source code, Edge device and Baseline, bridging the gap between disciplines. His Control research integrates issues from Asset, Selection and Portfolio.

His research on Machine learning frequently links to adjacent areas such as Benchmark. His Classifier research extends to Deep learning, which is thematically connected. His Recurrent neural network study combines topics in areas such as Social media and Directed graph.

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

Local features are not lonely – Laplacian sparse coding for image classification

Shenghua Gao;Ivor Wai-Hung Tsang;Liang-Tien Chia;Peilin Zhao.
computer vision and pattern recognition (2010)

552 Citations

Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life

Giduthuri Sateesh Babu;Peilin Zhao;Xiao-Li Li.
database systems for advanced applications (2016)

354 Citations

Stochastic Optimization with Importance Sampling for Regularized Loss Minimization

Peilin Zhao;Peilin Zhao;Peilin Zhao;Tong Zhang;Tong Zhang.
international conference on machine learning (2015)

337 Citations

LIBOL: a library for online learning algorithms

Steven C. H. Hoi;Jialei Wang;Peilin Zhao.
Journal of Machine Learning Research (2014)

224 Citations

Online Feature Selection and Its Applications

Jialei Wang;Peilin Zhao;Steven C. H. Hoi;Rong Jin.
IEEE Transactions on Knowledge and Data Engineering (2014)

221 Citations

Online AUC Maximization

Peilin Zhao;Rong Jin;Tianbao Yang;Steven C. Hoi.
international conference on machine learning (2011)

195 Citations

Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.

Yong Liu;Yong Liu;Min Wu;Chunyan Miao;Peilin Zhao.
PLOS Computational Biology (2016)

186 Citations

Online multimodal deep similarity learning with application to image retrieval

Pengcheng Wu;Steven C.H. Hoi;Hao Xia;Peilin Zhao.
acm multimedia (2013)

183 Citations

Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization

Ali Ezzat;Peilin Zhao;Min Wu;Xiao-Li Li.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2017)

165 Citations

PAMR: Passive aggressive mean reversion strategy for portfolio selection

Bin Li;Peilin Zhao;Steven C. Hoi;Vivekanand Gopalkrishnan.
Machine Learning (2012)

133 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.

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Top Scientists Citing Peilin Zhao

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Steven C. H. Hoi

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South China University of Technology

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King Abdullah University of Science and Technology

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University of South Carolina

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Agency for Science, Technology and Research

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Xindong Wu

Xindong Wu

Hefei University of Technology

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Qi Tian

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Huawei Technologies (China)

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Zhi-Hua Zhou

Zhi-Hua Zhou

Nanjing University

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Tong Zhang

Tong Zhang

Hong Kong University of Science and Technology

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Tianbao Yang

Tianbao Yang

University of Iowa

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Bernard Ghanem

Bernard Ghanem

King Abdullah University of Science and Technology

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Junzhou Huang

Junzhou Huang

The University of Texas at Arlington

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A. Salman Avestimehr

A. Salman Avestimehr

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Qingming Huang

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Chinese Academy of Sciences

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Rong Jin

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