mlsanityData to 7 Oct 2026

Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference

arXiv 2407.02476 ↗First posted 2 Jul 2024cs.LG stat.MLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 25 of its 59 references that are in the map. First posted on arXiv 2 Jul 2024.

Builds on

25 of 59 references.

References · 25

  1. 2018Conditional Neural Processescited by 421
  2. 2013Gaussian Processes for Big Datacited by 377
  3. 2018Neural Processescited by 307
  4. 2019GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriescited by 230
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  6. 2017Doubly Stochastic Variational Inference for Deep Gaussian Processescited by 143
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  10. 2018Heterogeneous Multi-output Gaussian Process Predictioncited by 26
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  12. 2019Multi-task Learning for Aggregated Data using Gaussian Processescited by 20
13 more
  1. 2018Orthogonally Decoupled Variational Gaussian Processescited by 19
  2. 2020Sparse within Sparse Gaussian Processes using Neighbor Informationcited by 18
  3. 2017Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processescited by 14
  4. 2020Doubly Sparse Variational Gaussian Processescited by 14
  5. 2018The Gaussian Process Autoregressive Regression Model (GPAR)cited by 12
  6. 2022Nonstationary multi-output Gaussian processes via harmonizable spectral mixturescited by 8
  7. 2018Efficient Inference in Multi-task Cox Process Modelscited by 5
  8. 2021Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalizationcited by 5
  9. 2019A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Modelcited by 4
  10. 2022Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inferencecited by 4
  11. 2020A conditional one-output likelihood formulation for multitask Gaussian processescited by 3
  12. 2023Latent Variable Multi-output Gaussian Processes for Hierarchical Datasetscited by 3
  13. 2022Adaptive Cholesky Gaussian Processescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problemscited by 1
  2. 2026Transformed Latent Variable Multi-Output Gaussian Processescited by 0