This map shows the geographic impact of Ruosong 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 Ruosong Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ruosong Wang more than expected).
This network shows the impact of papers produced by Ruosong 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 Ruosong Wang. The network helps show where Ruosong Wang may publish in the future.
Co-authorship network of co-authors of Ruosong Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Ruosong Wang.
A scholar is included among the top collaborators of Ruosong 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 Ruosong Wang. Ruosong Wang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wang, Ruosong, et al.. (2021). An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap. arXiv (Cornell University). 34.1 indexed citations
5.
Wang, Yining, Ruosong Wang, Simon S. Du, & Akshay Krishnamurthy. (2021). Optimism in Reinforcement Learning with Generalized Linear Function Approximation. arXiv (Cornell University).2 indexed citations
6.
Wang, Ruosong, Simon S. Du, Lin F. Yang, & Sham M. Kakade. (2020). Is Long Horizon RL More Difficult Than Short Horizon RL. Neural Information Processing Systems. 33. 9075–9085.1 indexed citations
7.
Feng, Fei, Ruosong Wang, Wotao Yin, Simon S. Du, & Lin F. Yang. (2020). Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning. Neural Information Processing Systems. 33. 22492–22504.1 indexed citations
8.
Wang, Ruosong, Peilin Zhong, Simon S. Du, Russ R. Salakhutdinov, & Lin F. Yang. (2020). Planning with General Objective Functions: Going Beyond Total Rewards. Neural Information Processing Systems. 33. 14486–14497.2 indexed citations
9.
Wang, Ruosong, Ruslan Salakhutdinov, & Lin F. Yang. (2020). Provably Efficient Reinforcement Learning with General Value Function Approximation. arXiv (Cornell University).2 indexed citations
10.
Wang, Ruosong, Ruslan Salakhutdinov, & Lin F. Yang. (2020). Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension. arXiv (Cornell University). 33. 6123–6135.2 indexed citations
Arora, Sanjeev, Simon S. Du, Wei Hu, et al.. (2019). On Exact Computation with an Infinitely Wide Neural Net. arXiv (Cornell University). 32. 8139–8148.107 indexed citations
14.
Du, Simon S., Kangcheng Hou, Russ R. Salakhutdinov, et al.. (2019). Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. arXiv (Cornell University). 32. 5723–5733.16 indexed citations
15.
Clarkson, Kenneth L., Ruosong Wang, & David P. Woodruff. (2019). Dimensionality Reduction for Tukey Regression.. International Conference on Machine Learning. 1262–1271.
16.
Du, Simon S., et al.. (2019). Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle. Neural Information Processing Systems. 32. 8058–8068.4 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.