Robust priors for regularized regression

• URL: http://arxiv.org/abs/2010.02610v3
• Date: Mon, 25 Oct 2021 13:05:54 GMT
• Title: Robust priors for regularized regression
• Authors: Sebastian Bobadilla-Suarez, Matt Jones, Bradley C. Love
• Abstract summary: Penalized regression approaches like ridge regression shrink toward zero but zero weights is usually not a sensible prior. Inspired by simple and robust decisions humans use, we constructed non-zero priors for penalized regression models. Models with robust priors had excellent worst-case performance.
• Score: 12.945710636153537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Induction benefits from useful priors. Penalized regression approaches, like ridge regression, shrink weights toward zero but zero association is usually not a sensible prior. Inspired by simple and robust decision heuristics humans use, we constructed non-zero priors for penalized regression models that provide robust and interpretable solutions across several tasks. Our approach enables estimates from a constrained model to serve as a prior for a more general model, yielding a principled way to interpolate between models of differing complexity. We successfully applied this approach to a number of decision and classification problems, as well as analyzing simulated brain imaging data. Models with robust priors had excellent worst-case performance. Solutions followed from the form of the heuristic that was used to derive the prior. These new algorithms can serve applications in data analysis and machine learning, as well as help in understanding how people transition from novice to expert performance.

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