1 citations · 1 across the 5 of their papers we have counts for
7 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…
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…
Multilinear Common Component Analysis via Kronecker Product Representation
Kohei Yoshikawa, Shuichi Kawano
We consider the problem of extracting a common structure from multiple tensor datasets. For this purpose, we propose multilinear common component analysis (MCCA) based on Kronecker…
Relevance Vector Machine with Weakly Informative Hyperprior and Extended Predictive Information Criterion
Kazuaki. Murayama, Shuichi. Kawano
In the variational relevance vector machine, the gamma distribution is representative as a hyperprior over the noise precision of automatic relevance determination prior. Instead o…
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…