World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
11018
World Ranking
4352
National Ranking
2034

Overview

Quanquan Gu is affiliated with the University of California, Los Angeles in the United States. Their primary field of research is Computer Science, with a strong focus on several key subfields, including Artificial Intelligence, Management Science and Operations Research, Computer Vision and Pattern Recognition, Molecular Biology, and Computer Networks and Communications.

The scientist's work spans a range of prominent topics within these domains. These include Advanced Bandit Algorithms Research, Reinforcement Learning in Robotics, Adversarial Robustness in Machine Learning, Stochastic Gradient Optimization Techniques, Machine Learning and Algorithms, Machine Learning and Extreme Learning Machines (ELM), and Advanced Neural Network Applications.

Quanquan Gu has contributed extensively to academic literature, with a substantial number of publications appearing in notable venues. The most frequent publication platforms include arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), the Proceedings of the AAAI Conference on Artificial Intelligence, the Proceedings of the National Academy of Sciences, and Nature Communications.

Several recent papers highlight the range and focus of their work:

  • "Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States," 2022, Proceedings of the National Academy of Sciences
  • "On the Convergence and Robustness of Adversarial Training," 2021, arXiv (Cornell University)
  • "Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.," 2020, bioRxiv (Cold Spring Harbor Laboratory)
  • "Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States," 2020, bioRxiv (Cold Spring Harbor Laboratory)
  • "A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave," 2021, Nature Communications

Frequent collaborators in Quanquan Gu's research include Dongruo Zhou, Difan Zou, Weitong Zhang, Zixiang Chen, and Jinghui Chen. These co-authors have worked closely on numerous projects, with collaboration counts ranging from sixteen to over thirty joint works.

Best Publications

  • Personalized entity recommendation: a heterogeneous information network approach

    Xiao Yu;Xiang Ren;Yizhou Sun;Quanquan Gu

  • Generalized Fisher score for feature selection

    Quanquan Gu;Zhenhui Li;Jiawei Han

  • Gradient descent optimizes over-parameterized deep ReLU networks

    Difan Zou;Yuan Cao;Dongruo Zhou;Quanquan Gu

  • Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks

    Difan Zou;Yuan Cao;Dongruo Zhou;Quanquan Gu

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

    Quanquan Gu;Jie Zhou;Chris H. Q. Ding

  • Improving Adversarial Robustness Requires Revisiting Misclassified Examples

    Yisen Wang;Difan Zou;Jinfeng Yi;James Bailey

  • Co-clustering on manifolds

    Quanquan Gu;Jie Zhou

  • Recommendation in heterogeneous information networks with implicit user feedback

    Xiao Yu;Xiang Ren;Yizhou Sun;Bradley Sturt

  • Joint feature selection and subspace learning

    Quanquan Gu;Zhenhui Li;Jiawei Han

  • Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks

    Yuan Cao;Quanquan Gu

  • Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.

    Evan L Ray;Nutcha Wattanachit;Jarad Niemi;Abdul Hannan Kanji

  • On the convergence and robustness of adversarial training

    Yisen Wang;Xingjun Ma;James Bailey;Jinfeng Yi

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

    Quanquan Gu;Jie Zhou

  • Closing the generalization gap of adaptive gradient methods in training deep neural networks

    Jinghui Chen;Dongruo Zhou;Yiqi Tang;Ziyan Yang

  • Citation Prediction in Heterogeneous Bibliographic Networks.

    Xiao Yu;Quanquan Gu;Mianwei Zhou;Jiawei Han

  • ClusCite: effective citation recommendation by information network-based clustering

    Xiang Ren;Jialu Liu;Xiao Yu;Urvashi Khandelwal

  • Towards Understanding the Spectral Bias of Deep Learning

    Yuan Cao;Zhiying Fang;Yue Wu;Ding-Xuan Zhou

  • Is neuron coverage a meaningful measure for testing deep neural networks

    Fabrice Harel-Canada;Lingxiao Wang;Muhammad Ali Gulzar;Quanquan Gu

  • Clustered Support Vector Machines

    Quanquan Gu;Jiawei Han

  • Stochastic Nested Variance Reduction for Nonconvex Optimization

    Dongruo Zhou;Pan Xu;Quanquan Gu

  • Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization

    Bargav Jayaraman;Lingxiao Wang;David Evans;Quanquan Gu

  • Active Learning: A Survey

    Charu C. Aggarwal;Xiangnan Kong;Quanquan Gu;Jiawei Han

  • Generalization error bounds of gradient descent for learning over-parameterized deep relu networks

    Yuan Cao;Quanquan Gu

  • Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks

    Difan Zou;Ziniu Hu;Yewen Wang;Song Jiang

  • Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization

    Pan Xu;Jinghui Chen;Quanquan Gu

Frequent Co-Authors

Jiawei Han
Jiawei Han University of Illinois at Urbana-Champaign
Jie Zhou
Jie Zhou Tsinghua University
Han Liu
Han Liu Northwestern University
Zhenhui Li
Zhenhui Li Pennsylvania State University
David Evans
David Evans University of Virginia
Stanley Osher
Stanley Osher University of California, Los Angeles
Lihong Li
Lihong Li Amazon (United States)
Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign
Aleksandra B. Djurišić
Aleksandra B. Djurišić University of Hong Kong
W. Skorupa
W. Skorupa Helmholtz-Zentrum Dresden-Rossendorf

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