Task-Relevant Feature Selection with Prediction Focused Mixture Models

Published: 22 Jul 2024, Last Modified: 17 Sept 2024Accepted by TMLREveryoneRevisionsBibTeXCC BY 4.0
Abstract: Probabilistic models, such as mixture models, can encode latent structures that both explain the data and aid specific downstream tasks. We focus on a constrained setting where we want to learn a model with relatively few components (e.g. for interpretability). Simultaneously, we ensure that the components are useful for downstream predictions by introducing \emph{prediction-focused} modeling for mixtures, which automatically selects data features relevant to a prediction task. Our approach identifies task-relevant input features, outperforms models that are not prediction-focused, and is easy to optimize; most importantly, we also characterize \emph{when} prediction-focused modeling can be expected to work.
Submission Length: Long submission (more than 12 pages of main content)
Assigned Action Editor: ~Aditya_Menon1
Submission Number: 1919
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