H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 41 Citations 7,047 151 World Ranking 4251 National Ranking 2137

Overview

What is he best known for?

The fields of study he is best known for:

  • Machine learning
  • Statistics
  • Artificial intelligence

Quanquan Gu mostly deals with Artificial intelligence, Gradient descent, Algorithm, Pattern recognition and Feature selection. The Artificial intelligence study combines topics in areas such as Optimization problem and Machine learning. His Gradient descent study combines topics from a wide range of disciplines, such as Deep learning and Maxima and minima.

As a part of the same scientific study, Quanquan Gu usually deals with the Algorithm, concentrating on Upper and lower bounds and frequently concerns with Polynomial. His research in Pattern recognition intersects with topics in Subspace topology and Biclustering. Quanquan Gu interconnects Scoring algorithm and Integer programming in the investigation of issues within Feature selection.

His most cited work include:

  • Personalized entity recommendation: a heterogeneous information network approach (362 citations)
  • Generalized Fisher score for feature selection (210 citations)
  • Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks (196 citations)

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

Quanquan Gu mainly focuses on Artificial intelligence, Algorithm, Gradient descent, Applied mathematics and Mathematical optimization. The study incorporates disciplines such as Machine learning and Pattern recognition in addition to Artificial intelligence. The various areas that Quanquan Gu examines in his Algorithm study include Artificial neural network, Upper and lower bounds and Generalization.

In his works, Quanquan Gu conducts interdisciplinary research on Gradient descent and Initialization. His biological study spans a wide range of topics, including Sampling, Regularization, Estimator and Stochastic gradient descent. His Mathematical optimization study integrates concerns from other disciplines, such as Robustness and Benchmark.

He most often published in these fields:

  • Artificial intelligence (27.50%)
  • Algorithm (26.67%)
  • Gradient descent (19.17%)

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

  • Artificial intelligence (27.50%)
  • Artificial neural network (13.33%)
  • Regret (8.33%)

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

Quanquan Gu focuses on Artificial intelligence, Artificial neural network, Regret, Algorithm and Reinforcement learning. In most of his Artificial intelligence studies, his work intersects topics such as Machine learning. Many of his research projects under Artificial neural network are closely connected to Tangent and Quality with Tangent and Quality, tying the diverse disciplines of science together.

His work in the fields of Algorithm, such as Parameterized complexity, intersects with other areas such as Rate of convergence. His studies examine the connections between Reinforcement learning and genetics, as well as such issues in Discrete mathematics, with regards to Stationary point and Constraint. His Deep learning research includes elements of Normalization, Stochastic gradient descent, Regularization, Applied mathematics and Pattern recognition.

Between 2019 and 2021, his most popular works were:

  • Improving Adversarial Robustness Requires Revisiting Misclassified Examples (76 citations)
  • Gradient descent optimizes over-parameterized deep ReLU networks (50 citations)
  • A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks (30 citations)

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

  • Statistics
  • Machine learning
  • Artificial intelligence

His primary areas of investigation include Artificial intelligence, Algorithm, Adversarial system, Artificial neural network and Reinforcement learning. He has included themes like Optimization problem and Machine learning in his Artificial intelligence study. His work deals with themes such as Gradient descent and Generalization, which intersect with Algorithm.

His study in Adversarial system is interdisciplinary in nature, drawing from both Maximization, Norm, Leverage and Minification. His research investigates the link between Artificial neural network and topics such as Deep learning that cross with problems in Normalization, Pattern recognition and Kernel. His Reinforcement learning research is multidisciplinary, incorporating elements of Discrete mathematics, Logarithm and Dimension.

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

Personalized entity recommendation: a heterogeneous information network approach

Xiao Yu;Xiang Ren;Yizhou Sun;Quanquan Gu.
web search and data mining (2014)

448 Citations

Generalized Fisher score for feature selection

Quanquan Gu;Zhenhui Li;Jiawei Han.
uncertainty in artificial intelligence (2011)

382 Citations

Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks

Difan Zou;Yuan Cao;Dongruo Zhou;Quanquan Gu.
arXiv: Learning (2018)

293 Citations

Gradient descent optimizes over-parameterized deep ReLU networks

Difan Zou;Yuan Cao;Dongruo Zhou;Quanquan Gu.
Machine Learning (2020)

291 Citations

Collaborative filtering: Weighted nonnegative matrix factorization incorporating user and item graphs

Quanquan Gu;Jie Zhou;Chris H. Q. Ding.
siam international conference on data mining (2010)

261 Citations

Co-clustering on manifolds

Quanquan Gu;Jie Zhou.
knowledge discovery and data mining (2009)

240 Citations

Joint feature selection and subspace learning

Quanquan Gu;Zhenhui Li;Jiawei Han.
international joint conference on artificial intelligence (2011)

201 Citations

Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification

Quanquan Gu;Jie Zhou.
international conference on data mining (2009)

163 Citations

Recommendation in heterogeneous information networks with implicit user feedback

Xiao Yu;Xiang Ren;Yizhou Sun;Bradley Sturt.
conference on recommender systems (2013)

132 Citations

Citation Prediction in Heterogeneous Bibliographic Networks.

Xiao Yu;Quanquan Gu;Mianwei Zhou;Jiawei Han.
siam international conference on data mining (2012)

131 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 Quanquan Gu

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 38

Huan Liu

Huan Liu

Arizona State University

Publications: 31

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

Publications: 29

Feiping Nie

Feiping Nie

Northwestern Polytechnical University

Publications: 28

Zhihui Lai

Zhihui Lai

Shenzhen University

Publications: 25

Heng Huang

Heng Huang

University of Pittsburgh

Publications: 25

Chuan Shi

Chuan Shi

Beijing University of Posts and Telecommunications

Publications: 25

Jiliang Tang

Jiliang Tang

Michigan State University

Publications: 22

Yingbin Liang

Yingbin Liang

The Ohio State University

Publications: 20

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 19

Yizhou Sun

Yizhou Sun

University of California, Los Angeles

Publications: 19

Chris Ding

Chris Ding

Chinese University of Hong Kong, Shenzhen

Publications: 17

Masashi Sugiyama

Masashi Sugiyama

University of Tokyo

Publications: 17

James Bailey

James Bailey

University of Melbourne

Publications: 17

Han Liu

Han Liu

Princeton University

Publications: 15

Something went wrong. Please try again later.