This map shows the geographic impact of Zhaoran Wang'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 Zhaoran Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhaoran Wang more than expected).
This network shows the impact of papers produced by Zhaoran Wang. 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 Zhaoran Wang. The network helps show where Zhaoran Wang may publish in the future.
Co-authorship network of co-authors of Zhaoran Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Zhaoran Wang.
A scholar is included among the top collaborators of Zhaoran Wang 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 Zhaoran Wang. Zhaoran Wang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yang, Zhuoran, et al.. (2021). Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach. International Conference on Machine Learning. 3198–3207.3 indexed citations
7.
Yang, Zhuoran, et al.. (2021). Provably Efficient Actor-Critic for Risk-Sensitive and Robust Adversarial RL: A Linear-Quadratic Case. International Conference on Artificial Intelligence and Statistics. 2764–2772.2 indexed citations
Yang, Zhuoran, et al.. (2020). Dynamic regret of policy optimization in non-stationary environments. neural information processing systems. 33. 6743–6754.3 indexed citations
12.
Yang, Zhuoran, Chi Jin, Zhaoran Wang, Mengdi Wang, & Michael I. Jordan. (2020). Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations.. arXiv (Cornell University).2 indexed citations
13.
Yang, Zhuoran, Chi Jin, Zhaoran Wang, Mengdi Wang, & Michael I. Jordan. (2020). Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. Neural Information Processing Systems. 33. 13903–13916.3 indexed citations
14.
Wang, Lingxiao, Zhuoran Yang, & Zhaoran Wang. (2019). Statistical-Computational Tradeoff in Single Index Models. Neural Information Processing Systems. 32. 10419–10426.1 indexed citations
15.
Yang, Zhuoran, Yongxin Chen, Mingyi Hong, & Zhaoran Wang. (2019). Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost. neural information processing systems. 32. 8351–8363.17 indexed citations
16.
Cai, Qi, et al.. (2019). Neural proximal/trust region policy optimization attains globally optimal policy. neural information processing systems. 32.23 indexed citations
17.
Zhang, Kaiqing, Zhuoran Yang, & Zhaoran Wang. (2018). Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding. International Conference on Artificial Intelligence and Statistics. 258–268.3 indexed citations
Yang, Zhuoran, Zhaoran Wang, Han Liu, Yonina C. Eldar, & Tong Zhang. (2016). Sparse nonlinear regression: parameter estimation under nonconvexity. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 5. 2472–2481.14 indexed citations
20.
Wang, Zhaoran, et al.. (2016). Agnostic estimation for misspecified phase retrieval models. Journal of Machine Learning Research. 21. 1–39.5 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.