Niao He

1.4k total citations
30 papers, 383 citations indexed

About

Niao He is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computational Theory and Mathematics. According to data from OpenAlex, Niao He has authored 30 papers receiving a total of 383 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 7 papers in Management Science and Operations Research and 5 papers in Computational Theory and Mathematics. Recurrent topics in Niao He's work include Stochastic Gradient Optimization Techniques (6 papers), Bayesian Methods and Mixture Models (5 papers) and Reinforcement Learning in Robotics (5 papers). Niao He is often cited by papers focused on Stochastic Gradient Optimization Techniques (6 papers), Bayesian Methods and Mixture Models (5 papers) and Reinforcement Learning in Robotics (5 papers). Niao He collaborates with scholars based in United States, Switzerland and Bulgaria. Niao He's co-authors include Hua Ouyang, Alexander Gray, Le Song, Nan Du, Chenhui Shao, Yin Tian, Siyuan Chen, Yuquan Meng, Le Song and Xin Chen and has published in prestigious journals such as IEEE Transactions on Automatic Control, Operations Research and IEEE/ASME Transactions on Mechatronics.

In The Last Decade

Niao He

26 papers receiving 366 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Niao He United States 9 198 106 63 48 42 30 383
Chris Darken United States 7 170 0.9× 36 0.3× 54 0.9× 80 1.7× 26 0.6× 10 359
Yunwen Lei China 11 242 1.2× 111 1.0× 33 0.5× 97 2.0× 21 0.5× 51 355
Sashank J. Reddi United States 13 484 2.4× 102 1.0× 17 0.3× 106 2.2× 23 0.5× 30 592
Spyridon D. Mourtas Greece 15 291 1.5× 69 0.7× 255 4.0× 122 2.5× 163 3.9× 57 669
S. Somasundaram India 9 74 0.4× 49 0.5× 20 0.3× 62 1.3× 96 2.3× 61 430
Mikhail Posypkin Russia 9 65 0.3× 27 0.3× 78 1.2× 37 0.8× 17 0.4× 72 357
Zeyuan Allen-Zhu United States 11 301 1.5× 142 1.3× 16 0.3× 52 1.1× 61 1.5× 27 433
Akshay Krishnamurthy United States 13 293 1.5× 70 0.7× 20 0.3× 83 1.7× 73 1.7× 40 457
Ryohei Fujimaki Japan 12 310 1.6× 19 0.2× 65 1.0× 73 1.5× 50 1.2× 40 505
Michael Laszlo United States 9 345 1.7× 71 0.7× 19 0.3× 155 3.2× 21 0.5× 21 637

Countries citing papers authored by Niao He

Since Specialization
Citations

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

Fields of papers citing papers by Niao He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Niao He

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

All Works

20 of 20 papers shown
1.
He, Niao, et al.. (2025). Stochastic Optimization under Hidden Convexity. SIAM Journal on Optimization. 35(4). 2544–2571.
2.
3.
He, Niao, et al.. (2024). Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation. SIAM Journal on Optimization. 34(3). 2729–2755. 2 indexed citations
4.
5.
He, Niao, et al.. (2023). Sample Complexity and Overparameterization Bounds for Temporal-Difference Learning With Neural Network Approximation. IEEE Transactions on Automatic Control. 68(5). 2891–2905. 4 indexed citations
6.
He, Niao, et al.. (2023). Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. 2602–2609. 2 indexed citations
7.
8.
Chen, Xin, et al.. (2021). On the Bias-Variance-Cost Tradeoff of Stochastic Optimization. Neural Information Processing Systems. 34. 3 indexed citations
9.
Yang, Yingxiang, Negar Kiyavash, Le Song, & Niao He. (2020). The devil is in the detail: A framework for macroscopic prediction via microscopic models. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 33. 8006–8016. 1 indexed citations
10.
Lee, Donghwan & Niao He. (2020). A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms. neural information processing systems. 33. 15556–15567. 3 indexed citations
11.
Chen, Siyuan, et al.. (2020). Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery. IEEE/ASME Transactions on Mechatronics. 25(5). 2167–2176. 64 indexed citations
12.
Kiyavash, Negar, et al.. (2020). A Catalyst Framework for Minimax Optimization. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 33. 5667–5678. 5 indexed citations
13.
Dai, Bo, Hanjun Dai, Arthur Gretton, et al.. (2019). Kernel exponential family estimation via doubly dual embedding. UCL Discovery (University College London). 2321–2330. 1 indexed citations
14.
Yang, Yingxiang, Bo Dai, Negar Kiyavash, & Niao He. (2018). Predictive Approximate Bayesian Computation via Saddle Points. Neural Information Processing Systems. 31. 10260–10270. 1 indexed citations
15.
Dai, Bo, Hanjun Dai, Niao He, et al.. (2018). Coupled Variational Bayes via Optimization Embedding. Neural Information Processing Systems. 31. 9690–9700. 9 indexed citations
16.
Dai, Bo, et al.. (2017). Smoothed Dual Embedding Control.. arXiv (Cornell University). 2 indexed citations
17.
Yang, Yingxiang, et al.. (2017). Online Learning for Multivariate Hawkes Processes. neural information processing systems. 30. 4937–4946. 10 indexed citations
18.
Dai, Bo, Niao He, Hanjun Dai, & Le Song. (2016). Provable Bayesian Inference via Particle Mirror Descent. International Conference on Artificial Intelligence and Statistics. 985–994. 7 indexed citations
19.
He, Niao, Anatoli Juditsky, & Arkadi Nemirovski. (2015). Mirror Prox algorithm for multi-term composite minimization and semi-separable problems. Computational Optimization and Applications. 61(2). 275–319. 16 indexed citations
20.
Du, Nan, et al.. (2015). Time-sensitive recommendation from recurrent user activities. 28. 3492–3500. 68 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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