mlsanityData to 7 Oct 2026

Spike and slab variational Bayes for high dimensional logistic regression

arXiv 2010.11665 ↗First posted 22 Oct 2020stat.ML cs.LG math.STcited by 16

Not on the map. Why?

Abstract (arXiv)

Variational Bayes (VB) is a popular scalable alternative to Markov chain Monte Carlo for Bayesian inference. We study a mean-field spike and slab VB approximation of widely used Bayesian model selection priors in sparse high-dimensional logistic regression. We provide non-asymptotic theoretical guarantees for the VB posterior in both ℓ2 and prediction loss for a sparse truth, giving optimal (minimax) convergence rates. Since the VB algorithm does not depend on the unknown truth to achieve optimality, our results shed light on effective prior choices. We confirm the improved performance of our VB algorithm over common sparse VB approaches in a numerical study.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 3; 2024: 3; 2025: 3; 2026: 1.

16 papers.

Sort:

  1. 2022VICE: Variational Interpretable Concept Embeddingscited by 15
  2. 2021Variational Inference in high-dimensional linear regressioncited by 10
  3. 2022On the inability of Gaussian process regression to optimally learn compositional functionscited by 7
  4. 2022Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selectioncited by 7
  5. 2022Scalable Spike-and-Slabcited by 5
  6. 2023Machine Learning and the Future of Bayesian Computationcited by 4
  7. 2023A Mean Field Approach to Empirical Bayes Estimation in High-dimensional Linear Regressioncited by 4
  8. 2021Variational Bayes for high-dimensional proportional hazards models with applications within gene expressioncited by 3
  9. 2024Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Priorcited by 2
  10. 2023Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion priorcited by 1
  11. 2024Adaptive posterior concentration rates for sparse high-dimensional linear regression with random design and unknown error variancecited by 1
  12. 2024Logistic Variational Bayes Revisitedcited by 1
4 more
  1. 2025Variational empirical Bayes variable selection in high-dimensional logistic regressioncited by 0
  2. 2025Variational Task Vector Compositioncited by 0
  3. 2025BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Datacited by 0
  4. 2026A Flexible Empirical Bayes Approach to Generalized Linear Models, with Applications to Sparse Logistic Regressioncited by 0