Kensuke Tanioka

This study introduces a robust cooperative learning method for multiview regression that reduces outlier influence while preserving view information sharing, showing improved predictive performance in labor onset prediction.

The paper introduces CL-pLMM, a method for integrating clustered multiview biomedical data, and shows it improves prediction in longitudinal proteomic and metabolomic studies.

This study uses fNIRS to compare hemispheric lateralization between expert and non-expert dart players, finding higher lateralization in experts and younger individuals.

The paper proposes new treatment effect estimation methods using L0 norm-based lasso and ridge regression to identify subgroups of patients who may respond to a new treatment.

This study examines brain synchronization during economic exchanges between acquainted pairs, finding no increase in IBS compared to strangers but identifying pair-specific synchronization in specific brain regions during the task.

This study develops a model to predict distracted driving using fNIRS-measured brain activity, finding that specific neural network connections are key predictors of distraction levels.

The paper proposes a thresholding method for estimating sparse low-rank correlation matrices using the MM algorithm, showing improved performance over existing methods in simulations and real data.

The paper proposes a dimensional reduction clustering method for fMRI data by approximating the difference between correlation matrices using low rank approximation and ordinal k-means, showing improved performance in recovering true clustering structures.

This study uses Tucker3 clustering on graph theoretical metrics to show that breath-counting meditation increases clustering coefficients in eight brain regions, indicating network density changes.