Keisuke Yamazaki
- Artificial Intelligence top 10%
- Statistics and Probability top 5%
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Signal Processing
- Co-authors
- Sumio WatanabeMotoaki KawanabeMasashi SugiyamaKlaus‐Robert MüllerKenji NagataYoichi MotomuraYoshiyuki TomitaMotonobu Kanagawa
- Topics
- Bayesian Methods and Mixture Models (16 papers)Neural Networks and Applications (7 papers)Machine Learning and Algorithms (6 papers)
- Partner nations
- JapanGermanyUnited States
In The Last Decade
Keisuke Yamazaki
32 papers receiving 220 citations
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 192
- Statistics and Probability 68
- Control and Systems Engineering 23
- Computer Vision and Pattern Recognition 23
- Signal Processing 23
Countries citing papers authored by Keisuke Yamazaki
This map shows the geographic impact of Keisuke Yamazaki'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 Keisuke Yamazaki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keisuke Yamazaki more than expected).
Fields of papers citing papers by Keisuke Yamazaki
This network shows the impact of papers produced by Keisuke Yamazaki. 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 Keisuke Yamazaki. The network helps show where Keisuke Yamazaki may publish in the future.
Co-authorship network of co-authors of Keisuke Yamazaki
This figure shows the co-authorship network connecting the top 25 collaborators of Keisuke Yamazaki. A scholar is included among the top collaborators of Keisuke Yamazaki 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 Keisuke Yamazaki. Keisuke Yamazaki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | Intractable Likelihood Regression for Covariate Shift by Kernel Mean Embedding. | 0 |
| 5 | Kernel Recursive ABC: Point Estimation with Intractable Likelihood | 2 |
| 6 | 1 | |
| 7 | 2 | |
| 8 | 1 | |
| 9 | 10 | |
| 10 | 1 | |
| 11 | 7 | |
| 12 | Accuracy of latent variable estimation with the maximum likelihood estimator for partially observed hidden data | 1 |
| 13 | 7 | |
| 14 | A Theoretical Analysis of KL-type Generalization Error on Hidden Variable Distribution | 2 |
| 15 | 13 | |
| 16 | 17 | |
| 17 | 27 | |
| 18 | Stochastic Complexity and Newton Diagram | 1 |
| 19 | 72 | |
| 20 | A Probabilistic Algorithm to Calculate the Learning Curves of Hierarchical Learning Machines with Singularities | 6 |
About Keisuke Yamazaki
Keisuke Yamazaki is a scholar working on Statistics and Probability, Artificial Intelligence and Transportation, having authored 36 papers that have together received 234 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (16 papers), Neural Networks and Applications (7 papers) and Machine Learning and Algorithms (6 papers). The work is most often cited by research in Statistics and Probability (68 citations), Artificial Intelligence (192 citations) and Computational Mathematics (3 citations). Keisuke Yamazaki has collaborated with scholars based in Japan, Germany and United States. Frequent co-authors include Sumio Watanabe, Motoaki Kawanabe, Masashi Sugiyama, Klaus‐Robert Müller, Kenji Nagata, Yoichi Motomura, Yoshiyuki Tomita, Motonobu Kanagawa, Fumio Satō and Koichi Kobayashi. Their work appears in journals such as IEEE Access, Neurocomputing and Neural Networks.
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.