Yuqing Kong
- Artificial Intelligence top 10%
- Computer Science Applications top 10%
- Management Science and Operations Research
- Computer Vision and Pattern Recognition
- Civil and Structural Engineering
- Co-authors
- Grant SchoenebeckPeng CaoYizhou WangGeorg EsslEmily Mower ProvostShan YouTracy Xiao LiuJinshan Zhang
- Topics
- Mobile Crowdsensing and Crowdsourcing (7 papers)Auction Theory and Applications (4 papers)Advanced Bandit Algorithms Research (4 papers)
- Cited by
- Computer Science ApplicationsArtificial IntelligenceManagement Science and Operations Research
- Journals
- Journal of the ACMSociety for Industrial and Applied Mathematics eBooksarXiv (Cornell University)
- Partner nations
- ChinaUnited States
In The Last Decade
Yuqing Kong
10 papers receiving 140 citations
Peers
Comparison fields: 5 of 44
- Artificial Intelligence 113
- Computer Science Applications 39
- Management Science and Operations Research 37
- Computer Vision and Pattern Recognition 23
- Civil and Structural Engineering 12
Countries citing papers authored by Yuqing Kong
This map shows the geographic impact of Yuqing Kong'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 Yuqing Kong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuqing Kong more than expected).
Fields of papers citing papers by Yuqing Kong
This network shows the impact of papers produced by Yuqing Kong. 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 Yuqing Kong. The network helps show where Yuqing Kong may publish in the future.
Co-authorship network of co-authors of Yuqing Kong
This figure shows the co-authorship network connecting the top 25 collaborators of Yuqing Kong. A scholar is included among the top collaborators of Yuqing Kong 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 Yuqing Kong. Yuqing Kong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 0 | |
| 6 | 6 | |
| 7 | L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise | 72 |
| 8 | 4 | |
| 9 | 6 | |
| 10 | 9 | |
| 11 | 26 | |
| 12 | 7 | |
| 13 | 10 |
About Yuqing Kong
Yuqing Kong is a scholar working on Computer Science Applications, Management Science and Operations Research and Artificial Intelligence, having authored 13 papers that have together received 147 indexed citations. Recurring topics across this work include Mobile Crowdsensing and Crowdsourcing (7 papers), Auction Theory and Applications (4 papers) and Advanced Bandit Algorithms Research (4 papers). The work is most often cited by research in Computer Science Applications (39 citations), Artificial Intelligence (113 citations) and Management Science and Operations Research (37 citations). Yuqing Kong has collaborated with scholars based in China and United States. Frequent co-authors include Grant Schoenebeck, Peng Cao, Yizhou Wang, Georg Essl, Emily Mower Provost, Shan You, Tracy Xiao Liu, Jinshan Zhang, Jason D. Hartline and Zhihuan Huang. Their work appears in journals such as Journal of the ACM, Society for Industrial and Applied Mathematics eBooks and arXiv (Cornell University).
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.