# VIB is Half Bayes

arXiv: https://arxiv.org/abs/2011.08711 · HTML: https://mlsanity.com/paper/2011.08711/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Fully Bayesian VIB-DeepSSM](https://mlsanity.com/paper/2305.05797/) (2023)
- [Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Images](https://mlsanity.com/paper/2405.09697/) (2024)

## Abstract (arXiv)

In discriminative settings such as regression and classification there are two random variables at play, the inputs X and the targets Y. Here, we demonstrate that the Variational Information Bottleneck can be viewed as a compromise between fully empirical and fully Bayesian objectives, attempting to minimize the risks due to finite sampling of Y only. We argue that this approach provides some of the benefits of Bayes while requiring only some of the work.