Support Recovery with Stochastic Gates: Theory and Application for Linear Models
Abstract
Consider the problem of simultaneous estimation and support recovery of the coefficient vector in a linear data model with additive Gaussian noise. We study the problem of estimating the model coefficients based on a recently proposed non-convex regularizer, namely the stochastic gates (STG) [Yamada et al. 2020]. We suggest a new projection-based algorithm for solving the STG regularized minimization problem, and prove convergence and support recovery guarantees of the STG-estimator for a range of random and non-random design matrix setups. Our new algorithm has been shown to outperform the existing STG algorithm and other classical estimators for support recovery in various real and synthetic data analyses.
Cite
@article{arxiv.2110.15960,
title = {Support Recovery with Stochastic Gates: Theory and Application for Linear Models},
author = {Soham Jana and Henry Li and Yutaro Yamada and Ofir Lindenbaum},
journal= {arXiv preprint arXiv:2110.15960},
year = {2022}
}
Comments
15 pages, 7 figures, extended the theoretical studies and numerical analyses to other probabilistic models (subgaussian, moment constraints etc.)