Quanquan Gu
- Computational Mathematics top 2%
- Artificial Intelligence top 0.5%
- Stochastic Gradient Optimization Techniques 31
- Machine Learning and ELM 16
- Advanced Graph Neural Networks 12
- Domain Adaptation and Few-Shot Learning 11
- Adversarial Robustness in Machine Learning 9
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- Face and Expression Recognition 13
- Advanced Neural Network Applications 13
- Information Systems top 0.5%
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- Sparse and Compressive Sensing Techniques 29
- Journals
- Journal of Machine Learning Research (2 papers)Optics Express (1 paper)Cancer Imaging (1 paper)
- Partner nations
- United StatesChinaCanada
In The Last Decade
Quanquan Gu
139 papers receiving 3.7k citations
Hit Papers
Peers
Comparison fields: 5 of 171
- Computational Mathematics 53
- Artificial Intelligence 2.3k
- Computer Vision and Pattern Recognition 900
- Information Systems 894
- Statistical and Nonlinear Physics 399
Countries citing papers authored by Quanquan Gu
This map shows the geographic impact of Quanquan Gu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Quanquan Gu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Quanquan Gu more than expected).
Fields of papers citing papers by Quanquan Gu
This network shows the impact of papers produced by Quanquan Gu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Quanquan Gu. The network helps show where Quanquan Gu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Quanquan Gu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 5 | |
| 2 | 2024 | 5 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 0 | |
| 5 | 2021 | 49 | |
| 6 | The Benefits of Implicit Regularization from SGD in Least Squares Problems | 2021 | 1 |
| 7 | Improving Adversarial Robustness Requires Revisiting Misclassified Examples | 2020 | 153 |
| 8 | Improving Neural Language Generation with Spectrum Control | 2020 | 32 |
| 9 | 2019 | 121 | |
| 10 | Neural Contextual Bandits with Upper Confidence Bound-Based Exploration | 2019 | 3 |
| 11 | Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction | 2019 | 2 |
| 12 | Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima | 2018 | 1 |
| 13 | Robust Gaussian Graphical Model Estimation with Arbitrary Corruption | 2017 | 2 |
| 14 | Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization | 2017 | 4 |
| 15 | A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery | 2017 | 2 |
| 16 | Accelerated stochastic block coordinate gradient descent for sparsity constrained nonconvex optimization | 2016 | 11 |
| 17 | Optimal Statistical and Computational Rates for One Bit Matrix Completion | 2016 | 10 |
| 18 | 2014 | 66 | |
| 19 | Batch-mode active learning via error bound minimization | 2014 | 10 |
| 20 | Local learning regularized nonnegative matrix factorization | 2009 | 51 |
About Quanquan Gu
Quanquan Gu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistics and Probability, having authored 142 papers that have together received 3.8k indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (31 papers), Sparse and Compressive Sensing Techniques (29 papers), Machine Learning and ELM (16 papers), Face and Expression Recognition (13 papers), Advanced Neural Network Applications (13 papers), Advanced Graph Neural Networks (12 papers), Domain Adaptation and Few-Shot Learning (11 papers) and Adversarial Robustness in Machine Learning (9 papers). The work is most often cited by research in Computational Mathematics (53 citations), Artificial Intelligence (2.3k citations) and Computer Vision and Pattern Recognition (900 citations). Quanquan Gu has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Jiawei Han, Jie Zhou, Zhenhui Li, Xiao Yu, Urvashi Khandelwal, Yuan Cao, Xiang Ren, Bradley Sturt, Brandon Norick and Yizhou Sun. Their work appears in journals such as Journal of Machine Learning Research, Optics Express, Cancer Imaging, Nature Communications and European Journal of Neuroscience.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.