35 citations · 113 across the 20 of their papers we have counts for
12 papers · 1 filter
Multi-Task Learning Regression via Convex Clustering
Akira Okazaki, Shuichi Kawano
Multi-task learning (MTL) is a methodology that aims to improve the general performance of estimation and prediction by sharing common information among related tasks. In the MTL,…
Multivariate regression modeling in integrative analysis via sparse regularization
Shuichi Kawano, Toshikazu Fukushima, Junichi Nakagawa +1
The multivariate regression model basically offers the analysis of a single dataset with multiple responses. However, such a single-dataset analysis often leads to unsatisfactory r…
Bayesian Fused Lasso Modeling via Horseshoe Prior
Yuko Kakikawa, Kaito Shimamura, Shuichi Kawano
Bayesian fused lasso is one of the sparse Bayesian methods, which shrinks both regression coefficients and their successive differences simultaneously. In this paper, we propose a…
Multi-task Learning for Compositional Data via Sparse Network Lasso
Akira Okazaki, Shuichi Kawano
A network lasso enables us to construct a model for each sample, which is known as multi-task learning. Existing methods for multi-task learning cannot be applied to compositional…
A Bayesian approach to multi-task learning with network lasso
Kaito Shimamura, Shuichi Kawano
Network lasso is a method for solving a multi-task learning problem through the regularized maximum likelihood method. A characteristic of network lasso is setting a different mode…
Smoothly varying ridge regularization
Daeju Kim, Shuichi Kawano, Yoshiyuki Ninomiya
A basis expansion with regularization methods is much appealing to the flexible or robust nonlinear regression models for data with complex structures. When the underlying function…
Variable fusion for Bayesian linear regression via spike-and-slab priors
Shengyi Wu, Kaito Shimamura, Kohei Yoshikawa +2
In linear regression models, fusion of coefficients is used to identify predictors having similar relationships with a response. This is called variable fusion. This paper presents…
Bayesian generalized fused lasso modeling via NEG distribution
Kaito Shimamura, Masao Ueki, Shuichi Kawano +1
The fused lasso penalizes a loss function by the norm for both the regression coefficients and their successive differences to encourage sparsity of both. In this paper, we p…
Selection of tuning parameters in bridge regression models via Bayesian information criterion
Shuichi Kawano
We consider the bridge linear regression modeling, which can produce a sparse or non-sparse model. A crucial point in the model building process is the selection of adjusted parame…
Adaptive bridge regression modeling with model selection criteria
Shuichi Kawano
We consider the problem of constructing an adaptive bridge regression modeling, which is a penalized procedure by imposing different weights to different coefficients in the bridge…
Varying-coefficient modeling via regularized basis functions
Hidetoshi Matsui, Toshihiro Misumi, Shuichi Kawano
We address the problem of constructing varying-coefficient models based on basis expansions along with the technique of regularization. A crucial point in our modeling procedure is…
Semi-supervised logistic discrimination for functional data
Shuichi Kawano, Sadanori Konishi
Multi-class classification methods based on both labeled and unlabeled functional data sets are discussed. We present a semi-supervised logistic model for classification in the con…