# Support Recovery with Stochastic Gates: Theory and Application for Linear Models

arXiv: https://arxiv.org/abs/2110.15960 · HTML: https://mlsanity.com/paper/2110.15960/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [Learning Sparse Neural Networks through L0 Regularization](https://mlsanity.com/paper/1712.01312/) (2017)
- [Sparse High-Dimensional Regression: Exact Scalable Algorithms and Phase Transitions](https://mlsanity.com/paper/1709.10029/) (2017)
- [A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning](https://mlsanity.com/paper/1808.05403/) (2018)
- [Feature Selection using Stochastic Gates](https://mlsanity.com/paper/1810.04247/) (2018)
- [Differentiable Unsupervised Feature Selection based on a Gated Laplacian](https://mlsanity.com/paper/2007.04728/) (2020)
- [MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance](https://mlsanity.com/paper/1806.10171/) (2018)

## Built on by (most cited first, 8 of 8)

- [Interpretable Deep Clustering for Tabular Data](https://mlsanity.com/paper/2306.04785/) (2023)
- [Contextual Feature Selection with Conditional Stochastic Gates](https://mlsanity.com/paper/2312.14254/) (2023)
- [Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settings](https://mlsanity.com/paper/2511.06961/) (2025)
- [FineGates: LLMs Finetuning with Compression using Stochastic Gates](https://mlsanity.com/paper/2412.12951/) (2024)
- [Train Less, Infer Faster: Efficient Model Finetuning and Compression via Structured Sparsity](https://mlsanity.com/paper/2602.09169/) (2026)
- [Sparse Binarization for Fast Keyword Spotting](https://mlsanity.com/paper/2406.06634/) (2024)
- [Conditional Deep Canonical Time Warping](https://mlsanity.com/paper/2412.18234/) (2024)
- [Uncovering a Winning Lottery Ticket with Continuously Relaxed Bernoulli Gates](https://mlsanity.com/paper/2603.08914/) (2026)

## Abstract (arXiv)

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.