Shohei Hido
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Statistics and Probability top 5%
- Computer Networks and Communications top 10%
- Co-authors
- Hisashi KashimaMasashi SugiyamaTakafumi KanamoriYutaka TakahashiYuta TsuboiSteffen BickelHiroyuki KawanoLiwei Wang
- Topics
- Anomaly Detection Techniques and Applications (6 papers)Fault Detection and Control Systems (4 papers)Face and Expression Recognition (3 papers)
- Journals
- Journal of Machine Learning ResearchKnowledge and Information SystemsStatistical Analysis and Data Mining The ASA Data Science Journal
- Partner nations
- JapanUnited StatesGermany
In The Last Decade
Shohei Hido
14 papers receiving 772 citations
Peers
Comparison fields: 5 of 98
- Artificial Intelligence 590
- Computer Vision and Pattern Recognition 151
- Signal Processing 128
- Statistics and Probability 118
- Computer Networks and Communications 83
Countries citing papers authored by Shohei Hido
This map shows the geographic impact of Shohei Hido'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 Shohei Hido with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shohei Hido more than expected).
Fields of papers citing papers by Shohei Hido
This network shows the impact of papers produced by Shohei Hido. 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 Shohei Hido. The network helps show where Shohei Hido may publish in the future.
Co-authorship network of co-authors of Shohei Hido
This figure shows the co-authorship network connecting the top 25 collaborators of Shohei Hido. A scholar is included among the top collaborators of Shohei Hido 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 Shohei Hido. Shohei Hido 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 | 4 | |
| 3 | 15 | |
| 4 | 0 | |
| 5 | 111 | |
| 6 | A Least-squares Approach to Direct Importance Estimation | 211 |
| 7 | 63 | |
| 8 | 25 | |
| 9 | 168 | |
| 10 | 58 | |
| 11 | Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection | 43 |
| 12 | 47 | |
| 13 | 27 | |
| 14 | 34 | |
| 15 | 18 |
About Shohei Hido
Shohei Hido is a scholar working on Signal Processing, Statistics and Probability and Artificial Intelligence, having authored 15 papers that have together received 825 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (6 papers), Fault Detection and Control Systems (4 papers) and Face and Expression Recognition (3 papers). The work is most often cited by research in Artificial Intelligence (590 citations), Statistics and Probability (118 citations) and Signal Processing (128 citations). Shohei Hido has collaborated with scholars based in Japan, United States and Germany. Frequent co-authors include Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori, Yutaka Takahashi, Yuta Tsuboi, Steffen Bickel, Hiroyuki Kawano, Liwei Wang, Jun Sese and Taiji Suzuki. Their work appears in journals such as Journal of Machine Learning Research, Knowledge and Information Systems and Statistical Analysis and Data Mining The ASA Data Science Journal.
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.