103 citations · 289 across the 36 of their papers we have counts for
36 papers
A new type of federated clustering: A non-model-sharing approach
Yuji Kawamata, Kaoru Kamijo, Masateru Kihira +5
In recent years, the growing need to leverage sensitive data across institutions has led to increased attention on federated learning (FL), a decentralized machine learning paradig…
Estimating Covariate-balanced Survival Curve in Distributed Data Environment using Data Collaboration Quasi-Experiment
Akihiro Toyoda, Yuji Kawamata, Tomoru Nakayama +3
The sharing of patient-level data necessary for covariate-adjusted survival analysis between medical institutions is difficult due to privacy protection restrictions. We propose a…
Data collaboration for causal inference from limited medical testing and medication data
Tomoru Nakayama, Yuji Kawamata, Akihiro Toyoda +7
Observational studies enable causal inferences when randomized controlled trials (RCTs) are not feasible. However, integrating sensitive medical data across multiple institutions i…
Block cross-interactive residual smoothing for Lanczos-type solvers for linear systems with multiple right-hand sides
Kensuke Aihara, Akira Imakura, Keiichi Morikuni
Lanczos-type solvers for large sparse linear systems often exhibit large oscillations in the residual norms. In finite precision arithmetic, large oscillations increase the residua…
FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration
Akira Imakura, Tetsuya Sakurai
Recently, federated learning has attracted much attention as a privacy-preserving integrated analysis that enables integrated analysis of data held by multiple institutions without…
New Solutions Based on the Generalized Eigenvalue Problem for the Data Collaboration Analysis
Yuta Kawakami, Yuichi Takano, Akira Imakura
In recent years, the accumulation of data across various institutions has garnered attention for the technology of confidential data analysis, which improves analytical accuracy by…
Estimation of conditional average treatment effects on distributed confidential data
Yuji Kawamata, Ryoki Motai, Yukihiko Okada +2
The estimation of conditional average treatment effects (CATEs) is an important topic in many scientific fields. CATEs can be estimated with high accuracy if data distributed acros…
Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings
Akihiro Mizoguchi, Anna Bogdanova, Akira Imakura +1
Given the time and expense associated with bringing a drug to market, numerous studies have been conducted to predict the properties of compounds based on their structure using mac…
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…
Complex moment-based methods for differential eigenvalue problems
Akira Imakura, Keiichi Morikuni, Akitoshi Takayasu
This paper considers computing partial eigenpairs of differential eigenvalue problems (DEPs) such that eigenvalues are in a certain region on the complex plane. Recently, based on…
mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations
Toyotaro Suzumura, Akiyoshi Sugiki, Hiroyuki Takizawa +30
The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability…
Verified eigenvalue and eigenvector computations using complex moments and the Rayleigh$\unicode{x2013}$Ritz procedure for generalized Hermitian eigenvalue problems
Akira Imakura, Keiichi Morikuni, Akitoshi Takayasu
We propose a verified computation method for eigenvalues in a region and the corresponding eigenvectors of generalized Hermitian eigenvalue problems. The proposed method uses compl…
Complex moment-based method with nonlinear transformation for computing large and sparse interior singular triplets
Akira Imakura, Tetsuya Sakurai
This paper considers computing interior singular triplets corresponding to the singular values in some interval. Based on the concept of the complex moment-based parallel eigensolv…
LSEC: Large-scale spectral ensemble clustering
Hongmin Li, Xiucai Ye, Akira Imakura +1
Ensemble clustering is a fundamental problem in the machine learning field, combining multiple base clusterings into a better clustering result. However, most of the existing metho…
Cross-interactive residual smoothing for global and block Lanczos-type solvers for linear systems with multiple right-hand sides
Kensuke Aihara, Akira Imakura, Keiichi Morikuni
Global and block Krylov subspace methods are efficient iterative solvers for large sparse linear systems with multiple right-hand sides. However, global or block Lanczos-type solve…
Divide-and-conquer based Large-Scale Spectral Clustering
Hongmin Li, Xiucai Ye, Akira Imakura +1
Spectral clustering is one of the most popular clustering methods. However, how to balance the efficiency and effectiveness of the large-scale spectral clustering with limited comp…
Accuracy and Privacy Evaluations of Collaborative Data Analysis
Akira Imakura, Anna Bogdanova, Takaya Yamazoe +2
Distributed data analysis without revealing the individual data has recently attracted significant attention in several applications. A collaborative data analysis through sharing…
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations
Anna Bogdanova, Akie Nakai, Yukihiko Okada +2
Dimensionality Reduction is a commonly used element in a machine learning pipeline that helps to extract important features from high-dimensional data. In this work, we explore an…
Interpretable collaborative data analysis on distributed data
Akira Imakura, Hiroaki Inaba, Yukihiko Okada +1
This paper proposes an interpretable non-model sharing collaborative data analysis method as one of the federated learning systems, which is an emerging technology to analyze distr…
Flexible subspace iteration with moments for an effective contour integration-based eigensolver
Sarah Huber, Yasunori Futamura, Martin Galgon +3
Contour integration schemes are a valuable tool for the solution of difficult interior eigenvalue problems. However, the solution of many large linear systems with multiple right h…
Multiclass spectral feature scaling method for dimensionality reduction
Momo Matsuda, Keiichi Morikuni, Akira Imakura +2
Irregular features disrupt the desired classification. In this paper, we consider aggressively modifying scales of features in the original space according to the label information…