Work

A Bayesian Approach to Multi-task Learning With Network Lasso

Year: 2025

Type: article

Abstract: Network lasso is a method for solving a multi-task learning problem through the regularized maximum likelihood method. Network lasso is characterized by setting a different model for each sample. The relationships among the models are represented by relational coefficients. A crucial issue in network lasso is determining appropriate values for the 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 our proposed method is demonstrated in a simulation study and a real data analysis.

Source: Bulletin of informatics and cybernetics

DOI: 10.5109/7343133

Authors: Kaito Shimamura, Shuichi Kawano

Institutions: NTT Advanced Technology Corporation (Japan), Kyushu University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: University of Tokyo, Japan Society for the Promotion of Science