A Bayesian approach to multi-task learning with network lasso

Kaito Shimamura; Shuichi Kawano

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arXiv · 2110.09040

A Bayesian approach to multi-task learning with network lasso

Kaito Shimamura·Shuichi Kawano

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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.

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Kaito Shimamura, Shuichi Kawano. 2021-10-18. A Bayesian approach to multi-task learning with network lasso. https://arxiv.org/abs/2110.09040

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