Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference

Xiaoyu Jiang, Sokratia Georgaka, Magnus Rattray, Mauricio A. Álvarez
7/2/2024
cs.LGstat.ML

No official code found. Best implementation: XiaoyuJiang17/GS-LVMOGP (1 star, MIT, last commit 2025-06-18).

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.

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Code Implementations(1)

XiaoyuJiang17/GS-LVMOGPCommunity68%
MIT

Official Implementation for "Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference" (TMLR 2025)

10PythonMay 27, 20251 years agoMIT

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Cite this paper

@article{jiang2024scalable,
  title  = {Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference},
  author = {Xiaoyu Jiang and Sokratia Georgaka and Magnus Rattray and Mauricio A. Álvarez},
  year   = {2024},
  eprint = {2407.02476},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.02476v2}
}

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