Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Personalized entity recommendation
2014472 citationsQuanquan Gu, Jiawei Han et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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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).
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 of co-authors of Quanquan Gu
This figure shows the co-authorship network connecting the top 25 collaborators of Quanquan Gu.
A scholar is included among the top collaborators of Quanquan Gu based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Quanquan Gu. Quanquan Gu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tang, Jing, et al.. (2021). Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 5065–5073.5 indexed citations
3.
Zou, Difan, Jingfeng Wu, Vladimir Braverman, et al.. (2021). The Benefits of Implicit Regularization from SGD in Least Squares Problems. arXiv (Cornell University). 34.1 indexed citations
4.
Xu, Pan, et al.. (2020). Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. arXiv (Cornell University).1 indexed citations
Wang, Bao, et al.. (2019). DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. arXiv (Cornell University). 328–351.1 indexed citations
9.
Cao, Yuan & Quanquan Gu. (2019). Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. arXiv (Cornell University). 32. 10835–10845.17 indexed citations
10.
Zhou, Dongruo, Pan Xu, & Quanquan Gu. (2018). Stochastic Nested Variance Reduction for Nonconvex Optimization. Journal of Machine Learning Research. 21(103). 1–3932.10 indexed citations
11.
Yu, Yaodong, Pan Xu, & Quanquan Gu. (2018). Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima. Neural Information Processing Systems. 31. 4525–4535.1 indexed citations
12.
Xu, Pan, Jian Ma, & Quanquan Gu. (2017). Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization. Neural Information Processing Systems. 30. 1933–1944.4 indexed citations
13.
Xu, Pan, et al.. (2017). Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference. International Conference on Machine Learning. 684–693.3 indexed citations
14.
Gu, Quanquan, et al.. (2016). Low-rank and sparse structure pursuit via alternating minimization. International Conference on Artificial Intelligence and Statistics. 600–609.26 indexed citations
15.
Xu, Pan, et al.. (2016). Forward backward greedy algorithms for multi-task learning with faster rates. Uncertainty in Artificial Intelligence. 735–744.3 indexed citations
Gu, Quanquan & Jiawei Han. (2013). Clustered Support Vector Machines. International Conference on Artificial Intelligence and Statistics. 31. 307–315.56 indexed citations
18.
Gu, Quanquan, Charų C. Aggarwal, & Jiawei Han. (2013). Unsupervised Link Selection in Networks. International Conference on Artificial Intelligence and Statistics. 31. 298–306.1 indexed citations
19.
Gu, Quanquan, Marina Danilevsky, Zhenhui Li, & Jiawei Han. (2012). Locality Preserving Feature Learning. International Conference on Artificial Intelligence and Statistics. 22. 477–485.7 indexed citations
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.