Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
Abstract
The Multi-Output Gaussian Process is is a popular tool for modelling data from multiple sources. A typical choice to build a covariance function for a MOGP is the Linear Model of Coregionalization (LMC) which parametrically models the covariance between outputs. The Latent Variable MOGP (LV-MOGP) generalises this idea by modelling the covariance between outputs using a kernel applied to latent variables, one per output, leading to a flexible MOGP model that allows efficient generalization to new outputs with few data points. Computational complexity in LV-MOGP grows linearly with the number of outputs, which makes it unsuitable for problems with a large number of outputs. In this paper, we propose a stochastic variational inference approach for the LV-MOGP that allows mini-batches for both inputs and outputs, making computational complexity per training iteration independent of the number of outputs.
Keywords
Cite
@article{arxiv.2407.02476,
title = {Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference},
author = {Xiaoyu Jiang and Sokratia Georgaka and Magnus Rattray and Mauricio A. Álvarez},
journal= {arXiv preprint arXiv:2407.02476},
year = {2025}
}