Zhaoran Wang

106 papers receiving 1.2k citations

Peers

Zhaoran Wang
Comparison fields: 5 of 150
  • Artificial Intelligence 513
  • Statistics and Probability 193
  • Cognitive Neuroscience 156
  • Management Science and Operations Research 144
  • Computer Networks and Communications 141
Replace Laura E. Brown with:
Laura E. Brown United States
Shang‐Ming Zhou United Kingdom
Harry Zhang United States
Alan Jović Croatia
Debo Cheng China
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Citations per field
00.5×4.7×
Laura E. Brown · 1×
Citations per year

Countries citing papers authored by Zhaoran Wang

Since Specialization
Citations

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).

Fields of papers citing papers by Zhaoran Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

All Works

20 of 20 papers shown
#WorkIndexed citations
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9 15
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Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL
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13 7
14
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games
7
15
Provably efficient neural GTD algorithm for off-policy learning
1
16
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory.
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17
High-dimensional Varying Index Coefficient Models via Stein's Identity
3
18
On the statistical rate of nonlinear recovery in generative models with heavy-tailed data
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19 14
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Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma
7

About Zhaoran Wang

Zhaoran Wang is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research, having authored 121 papers that have together received 1.3k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (19 papers), Statistical Methods and Inference (13 papers) and Advanced Bandit Algorithms Research (10 papers). The work is most often cited by research in Statistics and Probability (193 citations), Artificial Intelligence (513 citations) and Management Science and Operations Research (144 citations). Zhaoran Wang has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include David M. Blei, Zhuoran Yang, Yuchen Xie, Huiguang He, José R. Zubizarreta, Xuelin Ma, Shuang Qiu, Laurent Charlin, Dawen Liang and Mingyi Hong. Their work appears in journals such as Journal of the American Statistical Association, IEEE Transactions on Pattern Analysis and Machine Intelligence and Management Science.

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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