Hisashi Kashima

5.4k total citations · 1 hit paper
136 papers, 2.7k citations indexed

About

Hisashi Kashima is a scholar working on Artificial Intelligence, Computer Science Applications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Hisashi Kashima has authored 136 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 85 papers in Artificial Intelligence, 30 papers in Computer Science Applications and 15 papers in Computer Vision and Pattern Recognition. Recurrent topics in Hisashi Kashima's work include Mobile Crowdsensing and Crowdsourcing (27 papers), Machine Learning and Data Classification (17 papers) and Anomaly Detection Techniques and Applications (17 papers). Hisashi Kashima is often cited by papers focused on Mobile Crowdsensing and Crowdsourcing (27 papers), Machine Learning and Data Classification (17 papers) and Anomaly Detection Techniques and Applications (17 papers). Hisashi Kashima collaborates with scholars based in Japan, United States and Germany. Hisashi Kashima's 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 and has published in prestigious journals such as Bioinformatics, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Hisashi Kashima

123 papers receiving 2.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation

2007 398 citations Masashi Sugiyama, Hisashi Kashima et al. profile →

Peers

Hisashi Kashima

AI

CVPR

CSA

SP

MB

Kamalika Chaudhuri United States

Cecilia M. Procopiuc United States

William F. Punch United States

Ofer Dekel United States

Jinfeng Yi United States

Sewoong Oh United States

Ralf Herbrich United Kingdom

David Cohn United States

D. Sculley United States

Jingrui He United States

Kamalika Chaudhuri United States

Hisashi Kashima

2.1k ×1.2

AI

583 ×1.3

CVPR

286 ×0.9

CSA

164 ×0.6

SP

129 ×0.5

MB

Citations per year, relative to Hisashi Kashima Hisashi Kashima (= 1×) peers Kamalika Chaudhuri

Countries citing papers authored by Hisashi Kashima

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Kashima, Hisashi, et al.. (2025). Cognitive Biases in Large Language Models: A Survey and Mitigation Experiments. 1009–1011. 1 indexed citations

2.

Kimura, Akisato, et al.. (2025). Flexible Source-free Domain Generalization via Domain Prompt-Discriminator Collaborative Learning. 1–8.

3.

Kashima, Hisashi, et al.. (2025). Transfer learning with pre-trained conditional generative models. Machine Learning. 114(4). 1 indexed citations

4.

Kashima, Hisashi, et al.. (2025). Learning Location-Guided Time-Series Shapelets. IEEE Transactions on Knowledge and Data Engineering. 37(5). 2712–2726.

5.

Kashima, Hisashi, et al.. (2023). Causal Effect Estimation on Hierarchical Spatial Graph Data. 2145–2154. 2 indexed citations

6.

Li, Jiyi, et al.. (2023). Multiview Representation Learning from Crowdsourced Triplet Comparisons. arXiv (Cornell University). 3827–3836. 1 indexed citations

7.

Kashima, Hisashi, et al.. (2023). QTNet: Theory-based Queue Length Prediction for Urban Traffic. 4832–4841. 3 indexed citations

8.

Iwata, Tomoharu, et al.. (2022). Context-aware spatio-temporal event prediction via convolutional Hawkes processes. Machine Learning. 111(8). 2929–2950. 4 indexed citations

9.

Yamada, Makoto, et al.. (2020). Link Prediction on Multiple Graphs with Graph Embedding and Optimal Transport. 1 indexed citations

10.

Meunier, Laurent, et al.. (2019). Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family.. arXiv (Cornell University). 3 indexed citations

11.

Yamada, Makoto, et al.. (2019). Context-Regularized Neural Collaborative Filtering for Game App Recommendation.. Conference on Recommender Systems. 16–20. 1 indexed citations

12.

Fujiwara, Yasuhiro, et al.. (2019). Fast Sparse Group Lasso. Neural Information Processing Systems. 32. 1700–1708. 9 indexed citations

13.

Baba, Yukino, et al.. (2019). Probabilistic Modeling of Peer Correction and Peer Assessment.. Educational Data Mining. 5 indexed citations

14.

Sato, Issei, Hisashi Kashima, & Hiroshi Nakagawa. (2014). Latent Confusion Analysis by Normalized Gamma Construction. International Conference on Machine Learning. 1116–1124. 1 indexed citations

15.

Oyama, Satoshi, Yukino Baba, Yuko Sakurai, & Hisashi Kashima. (2013). Accurate integration of crowdsourced labels using workers' self-reported confidence scores. International Joint Conference on Artificial Intelligence. 2554–2560. 33 indexed citations

16.

Hayashi, Kohei, Takashi Takenouchi, Ryota Tomioka, & Hisashi Kashima. (2011). Self-measuring Similarity for Multi-task Gaussian Process. International Conference on Machine Learning. 145–153. 3 indexed citations

17.

Tomioka, Ryota, Kohei Hayashi, & Hisashi Kashima. (2010). On the extension of trace norm to tensors. arXiv (Cornell University). 17 indexed citations

18.

Kashima, Hisashi, Jianying Hu, Bonnie K. Ray, & Moninder Singh. (2008). K-means clustering of proportional data using L1 distance. Proceedings - International Conference on Pattern Recognition. 1–4. 19 indexed citations

19.

Kato, Tsuyoshi, Hisashi Kashima, Masashi Sugiyama, & Kiyoshi Asai. (2007). Multi-Task Learning via Conic Programming. Tokyo Tech Research Repository (Tokyo Institute of Technology). 20. 737–744. 62 indexed citations

20.

Kashima, Hisashi, et al.. (2002). Kernels for Semi-Structured Data. International Conference on Machine Learning. 291–298. 68 indexed citations

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.

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