  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 model for each sample. The relationships among the models
are represented by relational coefficients. A crucial issue in network lasso is
to provide appropriate values for these relational coefficients. In this paper,
we propose a Bayesian approach to solve multi-task learning problems by network
lasso. This approach allows us to objectively determine the relational
coefficients by Bayesian estimation. The effectiveness of the proposed method
is shown in a simulation study and a real data analysis.

Methodology;Machine Learning;Machine Learning