Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference

• URL: http://arxiv.org/abs/2407.02476v1
• Date: Tue, 2 Jul 2024 17:53:56 GMT
• Title: Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
• Authors: Xiaoyu Jiang, Sokratia Georgaka, Magnus Rattray, Mauricio A. Alvarez,
• Abstract summary: The Latent Variable MOGP (LV-MOGP) allows efficient generalization to new outputs with few data points. complexity in LV-MOGP grows linearly with the number of outputs. We propose a variational inference approach for the LV-MOGP that allows mini-batches for both inputs and outputs.
• Score: 2.1249213103048414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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