13 citations · 14 across the 5 of their papers we have counts for
5 papers
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…
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…
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 sparse convex clustering via global-local shrinkage priors
Kaito Shimamura, Shuichi Kawano
Sparse convex clustering is to cluster observations and conduct variable selection simultaneously in the framework of convex clustering. Although a weighted norm is usually e…
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…