A Bayesian approach to multi-task learning with network lasso

TL;DR

This paper introduces a Bayesian method for multi-task learning with network lasso, enabling automatic estimation of relational coefficients to improve model performance in various tasks.

Contribution

It presents a novel Bayesian framework for network lasso that objectively estimates relational coefficients, enhancing multi-task learning capabilities.

Findings

Effective in simulation studies

Successful application to real data

Improves relational coefficient estimation

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.