A BAYESIAN APPROACH TO MULTI-TASK LEARNING WITH NETWORK LASSO

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.