1 citations · 1 across the 4 of their papers we have counts for
5 papers
Geographically Regularized AUC-Maximizing Personalized Federated Learning
Mayu Hiraishi, Kensuke Tanioka, Toshio Shimokawa
Accurate diagnostic and risk-prediction models are important for supporting clinical decision-making during infectious disease outbreaks. However, privacy and governance requiremen…
Regularized Sparse Optimal Discriminant Clustering
Mayu Hiraishi, Kensuke Tanioka, Hiroshi Yadohisa
We propose a new method based on sparse optimal discriminant clustering (SODC), incorporating a penalty term into the scoring matrix based on convex clustering. With the addition o…
Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of main effects
Mayu Hiraishi, Ke Wan, Kensuke Tanioka +2
This study proposes a novel framework based on the RuleFit method to estimate Heterogeneous Treatment Effect (HTE) in a randomized clinical trial. To achieve this, we adopted S-lea…
Wilcoxon-type Multivariate Cluster Elastic Net
Mayu Hiraishi, Kensuke Tanioka, Hiroshi Yadohisa
We propose a method for high dimensional multivariate regression that is robust to random error distributions that are heavy-tailed or contain outliers, while preserving estimation…
Concordance Rate of a Four-Quadrant Plot for Repeated Measurements
Mayu Hiraishi, Kensuke Tanioka, Toshio Shimokawa
Before new clinical measurement methods are implemented in clinical practice, it must be confirmed whether their results are equivalent to those of existing methods. The agreement…