# Bayesian Regularization: From Tikhonov to Horseshoe

arXiv: https://arxiv.org/abs/1902.06269 · HTML: https://mlsanity.com/paper/1902.06269/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts](https://mlsanity.com/paper/2302.02931/) (2023)
- [A New Branch-and-Bound Pruning Framework for ℓ0-Regularized Problems](https://mlsanity.com/paper/2406.03504/) (2024)
- [Fast Compute for ML Optimization](https://mlsanity.com/paper/2602.14280/) (2026)

## Abstract (arXiv)

Bayesian regularization is a central tool in modern-day statistical and machine learning methods. Many applications involve high-dimensional sparse signal recovery problems. The goal of our paper is to provide a review of the literature on penalty-based regularization approaches, from Tikhonov (Ridge, Lasso) to horseshoe regularization.