Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918055429275648 |
|---|---|
| author | Jiang, Xiaoyu Georgaka, Sokratia Rattray, Magnus Álvarez, Mauricio A. |
| author_facet | Jiang, Xiaoyu Georgaka, Sokratia Rattray, Magnus Álvarez, Mauricio A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02476 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference Jiang, Xiaoyu Georgaka, Sokratia Rattray, Magnus Álvarez, Mauricio A. Machine Learning 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. |
| title | Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.02476 |