13 citations · 22 across the 11 of their papers we have counts for
4 papers · 1 filter
Achieving Transparency in Distributed Machine Learning with Explainable Data Collaboration
Anna Bogdanova, Akira Imakura, Tetsuya Sakurai +3
Transparency of Machine Learning models used for decision support in various industries becomes essential for ensuring their ethical use. To that end, feature attribution methods s…
Non-readily identifiable data collaboration analysis for multiple datasets including personal information
Akira Imakura, Tetsuya Sakurai, Yukihiko Okada +3
Multi-source data fusion, in which multiple data sources are jointly analyzed to obtain improved information, has considerable research attention. For the datasets of multiple medi…
Another Use of SMOTE for Interpretable Data Collaboration Analysis
Akira Imakura, Masateru Kihira, Yukihiko Okada +1
Recently, data collaboration (DC) analysis has been developed for privacy-preserving integrated analysis across multiple institutions. DC analysis centralizes individually construc…
Collaborative causal inference on distributed data
Yuji Kawamata, Ryoki Motai, Yukihiko Okada +2
In recent years, the development of technologies for causal inference with privacy preservation of distributed data has gained considerable attention. Many existing methods for dis…