# Scalable Gaussian Process Classification via Expectation Propagation

arXiv: https://arxiv.org/abs/1507.04513 · HTML: https://mlsanity.com/paper/1507.04513/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2015.

## Built on by (most cited first, 8 of 8)

- [Input Dependent Sparse Gaussian Processes](https://mlsanity.com/paper/2107.07281/) (2021)
- [Learning Choice Functions with Gaussian Processes](https://mlsanity.com/paper/2302.00406/) (2023)
- [An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products](https://mlsanity.com/paper/2107.11643/) (2021)
- [Active and sparse methods in smoothed model checking](https://mlsanity.com/paper/2104.09940/) (2021)
- [Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect](https://mlsanity.com/paper/2109.13891/) (2021)
- [Function-Space Regularization for Deep Bayesian Classification](https://mlsanity.com/paper/2307.06055/) (2023)
- [Fearless Stochasticity in Expectation Propagation](https://mlsanity.com/paper/2406.01801/) (2024)
- [Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process Classification](https://mlsanity.com/paper/2603.16621/) (2026)

## Abstract (arXiv)

Variational methods have been recently considered for scaling the training process of Gaussian process classifiers to large datasets. As an alternative, we describe here how to train these classifiers efficiently using expectation propagation. The proposed method allows for handling datasets with millions of data instances. More precisely, it can be used for (i) training in a distributed fashion where the data instances are sent to different nodes in which the required computations are carried out, and for (ii) maximizing an estimate of the marginal likelihood using a stochastic approximation of the gradient. Several experiments indicate that the method described is competitive with the variational approach.