A Bayesian approach to multi-task learning with network lasso
Methodology
2021-10-19 v1 Machine Learning
Machine Learning
Abstract
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.
Cite
@article{arxiv.2110.09040,
title = {A Bayesian approach to multi-task learning with network lasso},
author = {Kaito Shimamura and Shuichi Kawano},
journal= {arXiv preprint arXiv:2110.09040},
year = {2021}
}