Multi-Task Learning for Sparsity Pattern Heterogeneity: A Discrete Optimization Approach

1 code implementation • 16 Dec 2022 • Gabriel Loewinger, Kayhan Behdin, Kenneth T. Kishida, Giovanni Parmigiani, Rahul Mazumder

Allowing the regression coefficients of tasks to have different sparsity patterns (i. e., different supports), we propose a modeling framework for MTL that encourages models to share information across tasks, for a given covariate, through separately 1) shrinking the coefficient supports together, and/or 2) shrinking the coefficient values together.

Multi-Task Learning Variable Selection