Hisashi Kashima
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Molecular Biology
- Statistical and Nonlinear Physics top 5%
- Computational Theory and Mathematics top 5%
- Co-authors
- Koji TsudaAkihiro InokuchiMasashi SugiyamaTaiji SuzukiShinichi NakajimaPaul von BünauMotoaki KawanabeMasamichi Shimosaka
- Topics
- Mobile Crowdsensing and Crowdsourcing (7 papers)Reinforcement Learning in Robotics (3 papers)Advanced Graph Neural Networks (3 papers)
- Partner nations
- JapanGermanyUnited States
In The Last Decade
Hisashi Kashima
27 papers receiving 956 citations
Peers
Comparison fields: 5 of 115
- Artificial Intelligence 563
- Computer Vision and Pattern Recognition 234
- Molecular Biology 171
- Statistical and Nonlinear Physics 143
- Computational Theory and Mathematics 113
Countries citing papers authored by Hisashi Kashima
This map shows the geographic impact of Hisashi Kashima'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 Hisashi Kashima with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hisashi Kashima more than expected).
Fields of papers citing papers by Hisashi Kashima
This network shows the impact of papers produced by Hisashi Kashima. 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 Hisashi Kashima. The network helps show where Hisashi Kashima may publish in the future.
Co-authorship network of co-authors of Hisashi Kashima
This figure shows the co-authorship network connecting the top 25 collaborators of Hisashi Kashima. A scholar is included among the top collaborators of Hisashi Kashima 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 Hisashi Kashima. Hisashi Kashima 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 | 7 | |
| 3 | 3 | |
| 4 | 20 | |
| 5 | 24 | |
| 6 | 3 | |
| 7 | 3 | |
| 8 | 92 | |
| 9 | 5 | |
| 10 | 30 | |
| 11 | A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices | 15 |
| 12 | 11 | |
| 13 | 10 | |
| 14 | Nonparametric Return Distribution Approximation for Reinforcement Learning | 27 |
| 15 | 76 | |
| 16 | 18 | |
| 17 | 25 | |
| 18 | 4 | |
| 19 | 6 | |
| 20 | Marginalized kernels between labeled graphs | 371 |
About Hisashi Kashima
Hisashi Kashima is a scholar working on Computational Mathematics, Computer Science Applications and Artificial Intelligence, having authored 28 papers that have together received 1.0k indexed citations. Recurring topics across this work include Mobile Crowdsensing and Crowdsourcing (7 papers), Reinforcement Learning in Robotics (3 papers) and Advanced Graph Neural Networks (3 papers). The work is most often cited by research in Computer Science Applications (111 citations), Artificial Intelligence (563 citations) and Computational Mathematics (8 citations). Hisashi Kashima has collaborated with scholars based in Japan, Germany and United States. Frequent co-authors include Koji Tsuda, Akihiro Inokuchi, Masashi Sugiyama, Taiji Suzuki, Shinichi Nakajima, Paul von Bünau, Motoaki Kawanabe, Masamichi Shimosaka, Tsuyoshi Kato and Yoshihiro Yamanishi. Their work appears in journals such as Bioinformatics, Expert Systems with Applications and BMC Bioinformatics.
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.