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.
We present a method to estimate collections of regression models in which each model is personalized to a single sample.