Every Sample a Task: Pushing the Limits of Heterogeneous Models with Personalized Regression

Abstract

When data arise from multiple latent subpopulations, machine learning frameworks typically estimate parameter values independently for each sub-population. In this paper, we propose to overcome these limits by considering samples as tasks in a multitask learning framework.

Tasks

Reproductions