# Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO

arXiv: https://arxiv.org/abs/2010.06451 · HTML: https://mlsanity.com/paper/2010.06451/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [Information Directed Sampling for Sparse Linear Bandits](https://mlsanity.com/paper/2105.14267/) (2021)
- [Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes](https://mlsanity.com/paper/2111.04558/) (2021)
- [Scalable Spike-and-Slab](https://mlsanity.com/paper/2204.01668/) (2022)
- [Variational Bayes for high-dimensional proportional hazards models with applications within gene expression](https://mlsanity.com/paper/2112.10270/) (2021)
- [Sparse online variational Bayesian regression](https://mlsanity.com/paper/2102.12261/) (2021)
- [Spike-and-Slab LASSO Generalized Additive Models and Scalable Algorithms for High-Dimensional Data Analysis](https://mlsanity.com/paper/2110.14449/) (2021)
- [Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder](https://mlsanity.com/paper/2503.04386/) (2025)
- [Variational Task Vector Composition](https://mlsanity.com/paper/2509.18208/) (2025)
- [Testing-driven Variable Selection in Bayesian Modal Regression](https://mlsanity.com/paper/2510.23831/) (2025)
- [LLM Sparsity Prior for Robust Feature Selection](https://mlsanity.com/paper/2605.23102/) (2026)

## Abstract (arXiv)

High-dimensional data sets have become ubiquitous in the past few decades, often with many more covariates than observations. In the frequentist setting, penalized likelihood methods are the most popular approach for variable selection and estimation in high-dimensional data. In the Bayesian framework, spike-and-slab methods are commonly used as probabilistic constructs for high-dimensional modeling. Within the context of linear regression, Rockova and George (2018) introduced the spike-and-slab LASSO (SSL), an approach based on a prior which provides a continuum between the penalized likelihood LASSO and the Bayesian point-mass spike-and-slab formulations. Since its inception, the spike-and-slab LASSO has been extended to a variety of contexts, including generalized linear models, factor analysis, graphical models, and nonparametric regression. The goal of this paper is to survey the landscape surrounding spike-and-slab LASSO methodology. First we elucidate the attractive properties and the computational tractability of SSL priors in high dimensions. We then review methodological developments of the SSL and outline several theoretical developments. We illustrate the methodology on both simulated and real datasets.