Nonlinear Logistic Discrimination Via Regularized Gaussian Basis Expansions. - Shuichi Kawano; Sadanori Konishi (Commun. Stat. Simul. Comput., 2009)

Discovering functional gene pathways associated with cancer heterogeneity via sparse supervised learning. - Shuichi Kawano; Teppei Shimamura; Atsushi Niida; Seiya Imoto; Rui Yamaguchi; Masao Nagasaki; Ryo Yoshida; Cristin G. Print; Satoru Miyano (BIBM, 2010)

Semi-Supervised Logistic Discrimination Via Graph-Based Regularization. - Shuichi Kawano; Toshihiro Misumi; Sadanori Konishi (Neural Process. Lett., 2012)

Identifying Gene Pathways Associated with Cancer Characteristics via Sparse Statistical Methods. - Shuichi Kawano; Teppei Shimamura; Atsushi Niida; Seiya Imoto; Rui Yamaguchi; Masao Nagasaki; Ryo Yoshida; Cristin G. Print; Satoru Miyano (IEEE ACM Trans. Comput. Biol. Bioinform., 2012)

Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions. - Shuichi Kawano (Stat. Anal. Data Min., 2013)

Adaptive basis expansion via (ℓ1) trend filtering. - Daeju Kim; Shuichi Kawano; Yoshiyuki Ninomiya (Comput. Stat., 2014)

Constraint-Based Learning Bayesian Networks Using Bayes Factor. - Kazuki Natori; Masaki Uto; Yu Nishiyama; Shuichi Kawano; Maomi Ueno (AMBN@JSAI-isAI, 2015)

Sparse principal component regression with adaptive loading. - Shuichi Kawano; Hironori Fujisawa; Toyoyuki Takada; Toshihiko Shiroishi (Comput. Stat. Data Anal., 2015)

Sparse principal component regression for generalized linear models. - Shuichi Kawano; Hironori Fujisawa; Toyoyuki Takada; Toshihiko Shiroishi (Comput. Stat. Data Anal., 2018)

Relevance Vector Machine with Weakly Informative Hyperprior and Extended Predictive Information Criterion. - Kazuaki Murayama; Shuichi Kawano (CoRR, 2020)

Variable Fusion for Bayesian Linear Regression via Spike-and-slab Priors. - Shengyi Wu; Kaito Shimamura; Kohei Yoshikawa; Kazuaki Murayama; Shuichi Kawano (KES-IDT, 2021)

Discriminant Analysis via Smoothly Varying Regularization. - Hisao Yoshida; Shuichi Kawano; Yoshiyuki Ninomiya (KES-IDT, 2021)

Sparse principal component regression via singular value decomposition approach. - Shuichi Kawano (Adv. Data Anal. Classif., 2021)

A Bayesian approach to multi-task learning with network lasso. - Kaito Shimamura; Shuichi Kawano (CoRR, 2021)

Bayesian sparse convex clustering via global-local shrinkage priors. - Kaito Shimamura; Shuichi Kawano (Comput. Stat., 2021)

Multilinear Common Component Analysis via Kronecker Product Representation. - Kohei Yoshikawa; Shuichi Kawano (Neural Comput., 2021)

Multi-Task Learning for Compositional Data via Sparse Network Lasso. - Akira Okazaki; Shuichi Kawano (Entropy, 2022)

Smoothly varying regularization. - Daeju Kim; Shuichi Kawano; Yoshiyuki Ninomiya (Comput. Stat. Data Anal., 2023)

Sparse reduced-rank regression for simultaneous rank and variable selection via manifold optimization. - Kohei Yoshikawa; Shuichi Kawano (Comput. Stat., 2023)

Correction: Sparse reduced-rank regression for simultaneous rank and variable selection via manifold optimization. - Kohei Yoshikawa; Shuichi Kawano (Comput. Stat., 2023)

Sparse Bayesian Learning With Weakly Informative Hyperprior and Extended Predictive Information Criterion. - Kazuaki Murayama; Shuichi Kawano (IEEE Trans. Neural Networks Learn. Syst., 2023)

Multi-task learning regression via convex clustering. - Akira Okazaki; Shuichi Kawano (Comput. Stat. Data Anal., 2024)

Multi-task learning via robust regularized clustering with non-convex group penalties. - Akira Okazaki; Shuichi Kawano (Stat. Comput., 2025)