# Scalable Multi-Class Gaussian Process Classification using Expectation Propagation

arXiv: https://arxiv.org/abs/1706.07258 · HTML: https://mlsanity.com/paper/1706.07258/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2017.

## Built on by (most cited first, 5 of 5)

- [Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision](https://mlsanity.com/paper/2212.08997/) (2022)
- [Gaussian Processes for Missing Value Imputation](https://mlsanity.com/paper/2204.04648/) (2022)
- [Video-based Surgical Skill Assessment using Tree-based Gaussian Process Classifier](https://mlsanity.com/paper/2312.10208/) (2023)
- [Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process Classification](https://mlsanity.com/paper/2603.16621/) (2026)
- [Video-based Surgical Skill Assessment Using Dynamics-and-Uncertainty-Aware Tree-based Gaussian Process Classifier](https://mlsanity.com/paper/2609.24619/) (2026)

## Abstract (arXiv)

This paper describes an expectation propagation (EP) method for multi-class classification with Gaussian processes that scales well to very large datasets. In such a method the estimate of the log-marginal-likelihood involves a sum across the data instances. This enables efficient training using stochastic gradients and mini-batches. When this type of training is used, the computational cost does not depend on the number of data instances N. Furthermore, extra assumptions in the approximate inference process make the memory cost independent of N. The consequence is that the proposed EP method can be used on datasets with millions of instances. We compare empirically this method with alternative approaches that approximate the required computations using variational inference. The results show that it performs similar or even better than these techniques, which sometimes give significantly worse predictive distributions in terms of the test log-likelihood. Besides this, the training process of the proposed approach also seems to converge in a smaller number of iterations.