mlsanityData to 14 Oct 2026

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

arXiv 2110.15960 ↗First posted 29 Oct 2021math.ST cs.LG stat.MLcited by 8

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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.

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

9 of 45 references.

References · 9

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  3. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,148
  4. 2017Learning Sparse Neural Networks through L0 Regularizationcited by 583
  5. 2017Sparse High-Dimensional Regression: Exact Scalable Algorithms and Phase Transitionscited by 43
  6. 2018A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learningcited by 20
  7. 2018Feature Selection using Stochastic Gatescited by 12
  8. 2020Differentiable Unsupervised Feature Selection based on a Gated Laplaciancited by 2
  9. 2018MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonancecited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 1; 2026: 2.

8 papers.

  1. 2023Interpretable Deep Clustering for Tabular Datacited by 19
  2. 2023Contextual Feature Selection with Conditional Stochastic Gatescited by 7
  3. 2025Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settingscited by 5
  4. 2024FineGates: LLMs Finetuning with Compression using Stochastic Gatescited by 3
  5. 2026Train Less, Infer Faster: Efficient Model Finetuning and Compression via Structured Sparsitycited by 2
  6. 2024Sparse Binarization for Fast Keyword Spottingcited by 1
  7. 2024Conditional Deep Canonical Time Warpingcited by 0
  8. 2026Uncovering a Winning Lottery Ticket with Continuously Relaxed Bernoulli Gatescited by 0