6 papers
Interaction Tensor SHAP
Hiroki Hasegawa, Yukihiko Okada
This study proposes Interaction Tensor SHAP (IT-SHAP), a tensor algebraic formulation of the Shapley Taylor Interaction Index (STII) that makes its computational structure explicit…
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…
A Robust and Non-Iterative Tensor Decomposition Method with Automatic Thresholding
Hiroki Hasegawa, Yukihiko Okada
Recent advances in IoT and biometric sensing technologies have led to the generation of massive and high-dimensional tensor data, yet achieving accurate and efficient low-rank appr…
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…
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach
Sota Mashiko, Yuji Kawamata, Tomoru Nakayama +2
Anomaly detection is crucial in financial auditing, and effective detection requires large volumes of data from multiple organizations. However, journal entry data is highly sensit…
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…