Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression

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 that Nash can improve accuracy and adaptability over existing methods.