Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
<|reference_start|>Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference: 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.<|reference_end|>
@article{jiang2024scalable,
    title={Scalable Multi-Output Gaussian Processes with Stochastic Variational
  Inference},
    author={Xiaoyu Jiang, Sokratia Georgaka, Magnus Rattray, Mauricio A. Alvarez},
    journal={arXiv preprint arXiv:2407.02476},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02476},
    primaryClass={cs.LG stat.ML}
}