Hisashi Kashima
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 2%
- Computer Science Applications top 1%
- Signal Processing top 2%
- Molecular Biology
- Co-authors
- Masashi SugiyamaShohei HidoYukino BabaTsuyoshi IdéPaul von BünauShinichi NakajimaMotoaki KawanabeYuta Tsuboi
- Topics
- Mobile Crowdsensing and Crowdsourcing (27 papers)Machine Learning and Data Classification (17 papers)Anomaly Detection Techniques and Applications (17 papers)
- Partner nations
- JapanUnited StatesGermany
In The Last Decade
Hisashi Kashima
123 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 160
- Artificial Intelligence 1.7k
- Computer Vision and Pattern Recognition 459
- Computer Science Applications 336
- Signal Processing 256
- Molecular Biology 249
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 | 1 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 3 | |
| 8 | 4 | |
| 9 | 1 | |
| 10 | Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family. | 3 |
| 11 | Context-Regularized Neural Collaborative Filtering for Game App Recommendation. | 1 |
| 12 | Fast Sparse Group Lasso | 9 |
| 13 | Probabilistic Modeling of Peer Correction and Peer Assessment. | 5 |
| 14 | Latent Confusion Analysis by Normalized Gamma Construction | 1 |
| 15 | Accurate integration of crowdsourced labels using workers' self-reported confidence scores | 33 |
| 16 | Self-measuring Similarity for Multi-task Gaussian Process | 3 |
| 17 | On the extension of trace norm to tensors | 17 |
| 18 | 19 | |
| 19 | Multi-Task Learning via Conic Programming | 62 |
| 20 | Kernels for Semi-Structured Data | 68 |
About Hisashi Kashima
Hisashi Kashima is a scholar working on Computer Science Applications, Computational Mathematics and Artificial Intelligence, having authored 136 papers that have together received 2.7k indexed citations. Recurring topics across this work include Mobile Crowdsensing and Crowdsourcing (27 papers), Machine Learning and Data Classification (17 papers) and Anomaly Detection Techniques and Applications (17 papers). The work is most often cited by research in Computational Mathematics (143 citations), Computer Science Applications (336 citations) and Artificial Intelligence (1.7k citations). Hisashi Kashima has collaborated with scholars based in Japan, United States and Germany. Frequent co-authors include Masashi Sugiyama, Shohei Hido, Yukino Baba, Tsuyoshi Idé, Paul von Bünau, Shinichi Nakajima, Motoaki Kawanabe, Yuta Tsuboi, Jill-Jênn Vie and Yutaka Takahashi. Their work appears in journals such as Bioinformatics, Scientific Reports and Expert Systems with Applications.
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.