Hyokun Yun
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Computer Networks and Communications
- Computer Science Applications
- Co-authors
- S. V. N. VishwanathanHsiang‐Fu YuCho‐Jui HsiehInderjit S. DhillonJi Soo YiAnimashree AnandkumarZachary C. LiptonYakov Kronrod
- Topics
- Optimization and Search Problems (2 papers)Machine Learning and Algorithms (2 papers)Topic Modeling (2 papers)
- Cited by
- Computational MathematicsComputer Science ApplicationsComputer Vision and Pattern Recognition
- Journals
- ComputerProceedings of the VLDB EndowmentCaltechAUTHORS (California Institute of Technology)
- Partner nations
- United StatesJapanGermany
In The Last Decade
Hyokun Yun
7 papers receiving 153 citations
Peers
Comparison fields: 5 of 38
- Artificial Intelligence 92
- Computer Vision and Pattern Recognition 59
- Information Systems 53
- Computer Networks and Communications 22
- Computer Science Applications 16
Countries citing papers authored by Hyokun Yun
This map shows the geographic impact of Hyokun Yun'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 Hyokun Yun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hyokun Yun more than expected).
Fields of papers citing papers by Hyokun Yun
This network shows the impact of papers produced by Hyokun Yun. 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 Hyokun Yun. The network helps show where Hyokun Yun may publish in the future.
Co-authorship network of co-authors of Hyokun Yun
This figure shows the co-authorship network connecting the top 25 collaborators of Hyokun Yun. A scholar is included among the top collaborators of Hyokun Yun 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 Hyokun Yun. Hyokun Yun is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | Deep Active Learning for Named Entity Recognition. | 18 |
| 3 | 3 | |
| 4 | 17 | |
| 5 | Ranking via Robust Binary Classification | 14 |
| 6 | 79 | |
| 7 | 27 |
About Hyokun Yun
Hyokun Yun is a scholar working on Artificial Intelligence, Computer Science Applications and Management Science and Operations Research, having authored 7 papers that have together received 162 indexed citations. Recurring topics across this work include Optimization and Search Problems (2 papers), Machine Learning and Algorithms (2 papers) and Topic Modeling (2 papers). The work is most often cited by research in Computational Mathematics (10 citations), Computer Science Applications (16 citations) and Computer Vision and Pattern Recognition (59 citations). Hyokun Yun has collaborated with scholars based in United States, Japan and Germany. Frequent co-authors include S. V. N. Vishwanathan, Hsiang‐Fu Yu, Cho‐Jui Hsieh, Inderjit S. Dhillon, Ji Soo Yi, Animashree Anandkumar, Zachary C. Lipton, Yakov Kronrod, Yanyao Shen and Shihao Ji. Their work appears in journals such as Computer, Proceedings of the VLDB Endowment and CaltechAUTHORS (California Institute of Technology).
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.