A Bayesian approach to multi-task learning with network lasso
This addresses a crucial issue in multi-task learning for researchers, but it is incremental as it builds on existing network lasso methods.
The paper tackles the problem of determining relational coefficients in network lasso for multi-task learning by proposing a Bayesian approach, showing effectiveness in simulation and real data analysis.
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.