Kyle Hsu
Impact in
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- Formal Methods in Verification
- Petri Nets in System Modeling
Papers in
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- Logic, programming, and type systems 2
- Domain Adaptation and Few-Shot Learning 2
- Reinforcement Learning in Robotics 1
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- Multimodal Machine Learning Applications 1
- Co-authors
- Rupak Majumdar (2 shared papers)Anne-Kathrin Schmuck (2 shared papers)Kaushik Mallik (2 shared papers)Tae Joon Seok (1 shared paper)Ryan Going (1 shared paper)Ming C. Wu (1 shared paper)Chelsea Finn (2 shared papers)Sergey Levine (2 shared papers)
- Journals
- Optics Express (1 paper)Practice and Experience in Advanced Research Computing (1 paper)arXiv (Cornell University) (2 papers)International Conference on Artificial Intelligence and Statistics (1 paper)
- Partner nations
- CanadaUnited StatesSwitzerland
In The Last Decade
Kyle Hsu
8 papers receiving 91 citations
Peers
Comparison fields: 5 of 28
- Software 10
- Computational Theory and Mathematics 31
- Artificial Intelligence 39
- Information Systems and Management 8
- Acoustics and Ultrasonics 1
Countries citing papers authored by Kyle Hsu
This map shows the geographic impact of Kyle Hsu'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 Kyle Hsu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kyle Hsu more than expected).
Fields of papers citing papers by Kyle Hsu
This network shows the impact of papers produced by Kyle Hsu. 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 Kyle Hsu. The network helps show where Kyle Hsu may publish in the future.
Co-authors
The 20 scholars most cited alongside Kyle Hsu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 36 | |
| 2 | 2018 | 33 | |
| 3 | Unsupervised Learning via Meta-Learning | 2018 | 11 |
| 4 | 2019 | 6 | |
| 5 | 2022 | 5 | |
| 6 | 2022 | 5 | |
| 7 | On the role of data in PAC-Bayes | 2021 | 2 |
| 8 | Lazy Abstraction-Based Control for Reachability. | 2018 | 2 |
About Kyle Hsu
Kyle Hsu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Computer Networks and Communications and Atomic and Molecular Physics, and Optics, having authored 8 papers that have together received 100 indexed citations. Recurring topics across this work include Logic, programming, and type systems (2 papers), Formal Methods in Verification (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Semiconductor Quantum Structures and Devices (1 paper), Online Learning and Analytics (1 paper), Reinforcement Learning in Robotics (1 paper), Real-Time Systems Scheduling (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Software (10 citations), Computational Theory and Mathematics (31 citations), Artificial Intelligence (39 citations), Information Systems and Management (8 citations) and Acoustics and Ultrasonics (1 citation). Kyle Hsu has collaborated with scholars based in Canada, United States and Switzerland. Frequent co-authors include Rupak Majumdar, Anne-Kathrin Schmuck, Kaushik Mallik, Tae Joon Seok, Ryan Going, Ming C. Wu, Chelsea Finn, Sergey Levine, Marinus Pennings and Dhruva K. Chakravorty. Their work appears in journals such as Optics Express, Practice and Experience in Advanced Research Computing, arXiv (Cornell University) and International Conference on Artificial Intelligence and Statistics.
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.