mlsanityData to 14 Oct 2026

Heterogeneous Multi-output Gaussian Process Prediction

arXiv 1805.07633 ↗First posted 19 May 2018stat.ML cs.LGcited by 26

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Abstract (arXiv)

We present a novel extension of multi-output Gaussian processes for handling heterogeneous outputs. We assume that each output has its own likelihood function and use a vector-valued Gaussian process prior to jointly model the parameters in all likelihoods as latent functions. Our multi-output Gaussian process uses a covariance function with a linear model of coregionalisation form. Assuming conditional independence across the underlying latent functions together with an inducing variable framework, we are able to obtain tractable variational bounds amenable to stochastic variational inference. We illustrate the performance of the model on synthetic data and two real datasets: a human behavioral study and a demographic high-dimensional dataset.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 May 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 2; 2023: 5; 2024: 6; 2025: 2; 2026: 6.

26 papers.

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  1. 2023Robust and Conjugate Gaussian Process Regressioncited by 14
  2. 2021Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Featurescited by 8
  3. 2021Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regressioncited by 8
  4. 2022Learning Multi-Task Gaussian Process Over Heterogeneous Input Domainscited by 7
  5. 2023Bayesian Gaussian Process ODEs via Double Normalizing Flowscited by 7
  6. 2024Physics-Informed Variational State-Space Gaussian Processescited by 7
  7. 2021Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modellingcited by 6
  8. 2021Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalizationcited by 5
  9. 2023Heterogeneous Multi-Task Gaussian Cox Processescited by 4
  10. 2025JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMscited by 4
  11. 2023Latent Variable Multi-output Gaussian Processes for Hierarchical Datasetscited by 3
  12. 2024Federated Automatic Latent Variable Selection in Multi-output Gaussian Processescited by 3
14 more
  1. 2024Regularized Multi-output Gaussian Convolution Process with Domain Adaptationcited by 3
  2. 2022A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Datacited by 2
  3. 2024Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantificationcited by 2
  4. 2024Scalable Multi-Output Gaussian Processes with Stochastic Variational Inferencecited by 2
  5. 2025Mixed Likelihood Variational Gaussian Processescited by 2
  6. 2021Modular Gaussian Processes for Transfer Learningcited by 1
  7. 2024Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regressioncited by 1
  8. 2026Real-time Multi-instrument Autonomous Discovery of Novel Phase-change Memory Materialscited by 1
  9. 2023Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorizationcited by 0
  10. 2026Wasserstein-type Gaussian Process Regressions for Input Measurement Uncertaintycited by 0
  11. 2026Transformed Latent Variable Multi-Output Gaussian Processescited by 0
  12. 2026Elicitation-Augmented Bayesian Optimizationcited by 0
  13. 2026Forecasting Multiple Observables with SCROLL: Score-Trained Uncertainty for Stochastic Dynamicscited by 0
  14. 2026Residual Correlation as a Diagnostic for Joint-Uncertainty Gains from GP Coregionalisationcited by 0