Title: Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: Empirical Bayes, variational inference, split inference, high dimensional, penalized regression, penalty learning

Abstract: Sparse linear regression is a fundamental tool in data analysis. However, traditional approaches often fall short when covariates exhibit structure or arise from heterogeneous sources. In biomedical applications, covariates may stem from distinct modalities or be structured according to an underlying graph. We introduce Neural Adaptive Shrinkage (Nash), a unified framework that integrates covariate-specific side information into sparse regression via neural networks. Nash adaptively modulates penalties on a per-covariate basis, learning to tailor regularization without cross-validation. We develop a variational inference algorithm for efficient training and establish connections to empirical Bayes regression. Experiments on real data demonstrate Nash’s improved accuracy and adaptability over existing methods.

TL;DR: We propose Neural Adaptive Shrinkage (Nash), a sparse regression method that uses neural networks to learn covariate-specific penalties that we fit using a novel variational method that is very efficient.