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