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


May 16, 2019 Blind Submission readers: everyone
  • Keywords: Regression, Personalization, Multitask Learning
  • TL;DR: We present a method to estimate collections of regression models in which each model is personalized to a single sample.
  • 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.
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