# Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation

arXiv: https://arxiv.org/abs/1511.03405 · HTML: https://mlsanity.com/paper/1511.03405/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2015.

## Built on by (most cited first, 1 of 1)

- [Probabilistic Human Motion Prediction via A Bayesian Neural Network](https://mlsanity.com/paper/2107.06564/) (2021)

## Abstract (arXiv)

Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are probabilistic and non-parametric and as such are arguably more flexible, have a greater capacity to generalise, and provide better calibrated uncertainty estimates than alternative deep models. The focus of this paper is scalable approximate Bayesian learning of these networks. The paper develops a novel and efficient extension of probabilistic backpropagation, a state-of-the-art method for training Bayesian neural networks, that can be used to train DGPs. The new method leverages a recently proposed method for scaling Expectation Propagation, called stochastic Expectation Propagation. The method is able to automatically discover useful input warping, expansion or compression, and it is therefore is a flexible form of Bayesian kernel design. We demonstrate the success of the new method for supervised learning on several real-world datasets, showing that it typically outperforms GP regression and is never much worse.