# Nested Variational Compression in Deep Gaussian Processes

arXiv: https://arxiv.org/abs/1412.1370 · HTML: https://mlsanity.com/paper/1412.1370/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2014.

## Built on by (most cited first, 6 of 6)

- [Deep Neural Networks as Point Estimates for Deep Gaussian Processes](https://mlsanity.com/paper/2105.04504/) (2021)
- [A Sparse Expansion For Deep Gaussian Processes](https://mlsanity.com/paper/2112.05888/) (2021)
- [Convolutional Normalizing Flows for Deep Gaussian Processes](https://mlsanity.com/paper/2104.08472/) (2021)
- [Sparse Techniques for Regression in Deep Gaussian Processes](https://mlsanity.com/paper/2505.11355/) (2025)
- [Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression](https://mlsanity.com/paper/2107.08265/) (2021)
- [Deep Gaussian Processes on Directed Acyclic Graphs](https://mlsanity.com/paper/2607.09645/) (2026)

## Abstract (arXiv)

Deep Gaussian processes provide a flexible approach to probabilistic modelling of data using either supervised or unsupervised learning. For tractable inference approximations to the marginal likelihood of the model must be made. The original approach to approximate inference in these models used variational compression to allow for approximate variational marginalization of the hidden variables leading to a lower bound on the marginal likelihood of the model [Damianou and Lawrence, 2013]. In this paper we extend this idea with a nested variational compression. The resulting lower bound on the likelihood can be easily parallelized or adapted for stochastic variational inference.