Bingyi Kang

82 papers receiving 2.1k citations

Hit Papers

Depth Anything: Unleashing the Power of Large-Scale Unlab...202420262025202450100150200250

Peers

Bingyi Kang
Comparison fields: 5 of 125
  • Management Science and Operations Research 1.0k
  • Artificial Intelligence 813
  • Computational Theory and Mathematics 361
  • Control and Systems Engineering 348
  • Computer Vision and Pattern Recognition 320
Replace Joaquin Quiñonero-Candela with:
Joaquin Quiñonero-Candela Germany
Xinyang Deng China
Oleg A. Prokopyev United States
Deyun Zhou China
Emilio Carrizosa Spain
Kathrin Klamroth Germany
Hongxing Li China
James C. Spall United Kingdom
Katta G. Murty United States
Éloi Bossé Canada
Bingyi Kang relative to Joaquin Quiñonero-Candela Germany Joaquin Quiñonero-Candela's profile →
Citations per field
00.5×5.6×
Joaquin Quiñonero-Candela · 1×
Citations per year

Countries citing papers authored by Bingyi Kang

Since Specialization
Citations

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

Fields of papers citing papers by Bingyi Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bingyi Kang

This figure shows the co-authorship network connecting the top 25 collaborators of Bingyi Kang. A scholar is included among the top collaborators of Bingyi Kang 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 Bingyi Kang. Bingyi Kang 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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Improving Generalization in Reinforcement Learning with Mixture Regularization
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Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms
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Policy Optimization with Demonstrations
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About Bingyi Kang

Bingyi Kang is a scholar working on Management Science and Operations Research, Statistics and Probability and Computational Mathematics, having authored 92 papers that have together received 2.2k indexed citations. Recurring topics across this work include Multi-Criteria Decision Making (45 papers), Fuzzy Systems and Optimization (16 papers) and Rough Sets and Fuzzy Logic (12 papers). The work is most often cited by research in Management Science and Operations Research (1.0k citations), Statistics and Probability (316 citations) and Artificial Intelligence (813 citations). Bingyi Kang has collaborated with scholars based in China, Canada and Singapore. Frequent co-authors include Yong Deng, Rehan Sadiq, Ye Tian, Kasun Hewage, Jiashi Feng, Huizi Cui, Xiaogang Xu, Hengshuang Zhao, Lihe Yang and Zilong Huang. Their work appears in journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Access.

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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