This map shows the geographic impact of Yining 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 Yining Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yining Wang more than expected).
This network shows the impact of papers produced by Yining 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 Yining Wang. The network helps show where Yining Wang may publish in the future.
Co-authorship network of co-authors of Yining Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Yining Wang.
A scholar is included among the top collaborators of Yining 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 Yining Wang. Yining Wang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
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
Rudin, Cynthia & Yining Wang. (2018). Direct Learning to Rank And Rerank. International Conference on Artificial Intelligence and Statistics. 775–783.1 indexed citations
Wang, Yining & Animashree Anandkumar. (2016). Online and differentially-private tensor decomposition. CaltechAUTHORS (California Institute of Technology). 29. 3539–3547.5 indexed citations
20.
Wang, Yining, Liwei Wang, Yuanzhi Li, Di He, & Tie‐Yan Liu. (2013). A Theoretical Analysis of NDCG Type Ranking Measures. Conference on Learning Theory. 25–54.61 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.