Dongruo Zhou

1.2k total citations
10 papers, 208 citations indexed

About

Dongruo Zhou is a scholar working on Artificial Intelligence, Computational Mechanics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Dongruo Zhou has authored 10 papers receiving a total of 208 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 7 papers in Computational Mechanics and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Dongruo Zhou's work include Sparse and Compressive Sensing Techniques (7 papers), Stochastic Gradient Optimization Techniques (7 papers) and Advanced Neural Network Applications (3 papers). Dongruo Zhou is often cited by papers focused on Sparse and Compressive Sensing Techniques (7 papers), Stochastic Gradient Optimization Techniques (7 papers) and Advanced Neural Network Applications (3 papers). Dongruo Zhou collaborates with scholars based in United States and Canada. Dongruo Zhou's co-authors include Quanquan Gu, Yuan Cao, Difan Zou, Pan Xu, Jinghui Chen, Jinfeng Yi, Ziyan Yang, Lihong Li and Yuan Cao and has published in prestigious journals such as Machine Learning, Journal of Machine Learning Research and arXiv (Cornell University).

In The Last Decade

Dongruo Zhou

10 papers receiving 198 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Dongruo Zhou United States	6	150	49	48	22	21	10	208
Amir‐massoud Farahmand United States	9	84 0.6×	55 1.1×	13 0.3×	19 0.9×	12 0.6×	23	204
Roi Livni Israel	6	116 0.8×	48 1.0×	20 0.4×	9 0.4×	15 0.7×	17	187
Zheng Qu Hong Kong	9	135 0.9×	12 0.2×	88 1.8×	4 0.2×	43 2.0×	25	226
Ilija Bogunovic Switzerland	8	100 0.7×	22 0.4×	41 0.9×	6 0.3×	20 1.0×	19	182
Ashia Wilson United States	5	106 0.7×	50 1.0×	27 0.6×	8 0.4×	2 0.1×	8	173
Jajati Keshari Sahoo India	9	71 0.5×	21 0.4×	12 0.3×	15 0.7×	14 0.7×	27	176
Xinyi Xu China	9	72 0.5×	72 1.5×	21 0.4×	2 0.1×	32 1.5×	17	175
Xinjia Chen United States	9	30 0.2×	24 0.5×	9 0.2×	6 0.3×	27 1.3×	46	202
Hanie Sedghi United States	7	82 0.5×	35 0.7×	23 0.5×	4 0.2×	54 2.6×	15	167
Salar Fattahi United States	9	37 0.2×	7 0.1×	33 0.7×	8 0.4×	76 3.6×	26	208

Countries citing papers authored by Dongruo Zhou

Since Specialization

Citations

This map shows the geographic impact of Dongruo Zhou'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 Dongruo Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dongruo Zhou more than expected).

Fields of papers citing papers by Dongruo Zhou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dongruo Zhou. 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 Dongruo Zhou. The network helps show where Dongruo Zhou may publish in the future.

Co-authorship network of co-authors of Dongruo Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Dongruo Zhou. A scholar is included among the top collaborators of Dongruo Zhou 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 Dongruo Zhou. Dongruo Zhou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Zhou, Dongruo, Yuan Cao, & Quanquan Gu. (2020). Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. International Conference on Artificial Intelligence and Statistics. 4430–4440. 2 indexed citations

2.

Chen, Jinghui, Dongruo Zhou, Jinfeng Yi, & Quanquan Gu. (2020). A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 3486–3494. 18 indexed citations

3.

Chen, Jinghui, et al.. (2020). Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. 3267–3275. 30 indexed citations

4.

Zhou, Dongruo, Lihong Li, & Quanquan Gu. (2019). Neural Contextual Bandits with Upper Confidence Bound-Based Exploration. arXiv (Cornell University). 3 indexed citations

5.

Zhou, Dongruo, Pan Xu, & Quanquan Gu. (2019). Stochastic Variance-Reduced Cubic Regularization Methods. Journal of Machine Learning Research. 20(134). 1–47. 5 indexed citations

6.

Zou, Difan, Yuan Cao, Dongruo Zhou, & Quanquan Gu. (2019). Gradient descent optimizes over-parameterized deep ReLU networks. Machine Learning. 109(3). 467–492. 121 indexed citations

7.

Zhou, Dongruo & Quanquan Gu. (2019). Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. International Conference on Machine Learning. 7574–7583. 1 indexed citations

8.

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

9.

Zhou, Dongruo, Pan Xu, & Quanquan Gu. (2018). Stochastic Variance-Reduced Cubic Regularized Newton Methods. International Conference on Machine Learning. 5990–5999. 7 indexed citations

10.

Zhou, Dongruo, Pan Xu, & Quanquan Gu. (2018). Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization.. Neural Information Processing Systems. 3925–3936. 11 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.

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