mlsanityData to 7 Oct 2026

Multi-Output Robust and Conjugate Gaussian Processes

arXiv 2510.26401 ↗First posted 30 Oct 2025stat.ML cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

Multi-output Gaussian process (MOGP) regression allows modelling dependencies among multiple correlated response variables. Similarly to standard Gaussian processes, MOGPs are sensitive to model misspecification and outliers, which can distort predictions within individual outputs. This situation can be further exacerbated by multiple anomalous response variables whose errors propagate due to correlations between outputs. To handle this situation, we extend and generalise the robust and conjugate Gaussian process (RCGP) framework introduced by Altamirano et al. (2024). This results in the multi-output RCGP (MO-RCGP): a provably robust MOGP that is conjugate, and jointly captures correlations across outputs. We thoroughly evaluate our approach through applications in finance and cancer research.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 40 of its 88 references that are in the map. First posted on arXiv 30 Oct 2025.

Builds on

40 of 88 references.

References · 40

  1. 2013A General Framework for Updating Belief Distributionscited by 172
  2. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 156
  3. 2016GPflow: A Gaussian process library using TensorFlowcited by 139
  4. 2019Minimum Stein Discrepancy Estimatorscited by 57
  5. 2021Robust Generalised Bayesian Inference for Intractable Likelihoodscited by 53
  6. 2021Martingale posterior distributionscited by 43
  7. 2022Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrapcited by 41
  8. 2015Robust Bayesian inference via coarseningcited by 32
  9. 2021Bayesian Optimization with High-Dimensional Outputscited by 32
  10. 2023A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methodscited by 31
  11. 2017Spectral Mixture Kernels for Multi-Output Gaussian Processescited by 29
  12. 2020A comparison of learning rate selection methods in generalized Bayesian inferencecited by 23
28 more
  1. 2018Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergencescited by 21
  2. 2024Outlier-robust Kalman Filtering through Generalised Bayescited by 21
  3. 2019Estimating Density Models with Truncation Boundaries using Score Matchingcited by 20
  4. 2023Robust and Scalable Bayesian Online Changepoint Detectioncited by 16
  5. 2017Variational Inference based on Robust Divergencescited by 15
  6. 2018Bayesian Quadrature for Multiple Related Integralscited by 15
  7. 2017Multivariate Gaussian and Student-t Process Regression for Multi-output Predictioncited by 14
  8. 2021General Bayesian Loss Function Selection and the use of Improper Modelscited by 14
  9. 2022Generalised Bayesian Inference for Discrete Intractable Likelihoodcited by 14
  10. 2023Robust and Conjugate Gaussian Process Regressioncited by 14
  11. 2022Safe Active Learning for Multi-Output Gaussian Processescited by 13
  12. 2019Financial Applications of Gaussian Processes and Bayesian Optimizationcited by 12
  13. 2021Vector-Valued Control Variatescited by 12
  14. 2025Robust and Conjugate Spatio-Temporal Gaussian Processescited by 9
  15. 2015Cross-Covariance Functions for Multivariate Geostatisticscited by 8
  16. 2022Nonstationary multi-output Gaussian processes via harmonizable spectral mixturescited by 8
  17. 2024Prediction-Centric Uncertainty Quantification via MMDcited by 8
  18. 2020Generalized Multi-Output Gaussian Process Censored Regressioncited by 7
  19. 2024On the Robustness of Kernel Goodness-of-Fit Testscited by 7
  20. 2024Robust Gaussian Processes via Relevance Pursuitcited by 7
  21. 2023Robustifying likelihoods by optimistically re-weighting datacited by 6
  22. 2022Generalized Score Matching for Regressioncited by 5
  23. 2021Robust Variable Selection under Cellwise Contaminationcited by 4
  24. 2019Neural Likelihoods for Multi-Output Gaussian Processescited by 3
  25. 2020Gaussian process imputation of multiple financial seriescited by 3
  26. 2017Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testingcited by 2
  27. 2023Multi-Response Heteroscedastic Gaussian Process Models and Their Inferencecited by 2
  28. 2024Causal-ICM: A Data Fusion Framework For Heterogeneous Treatment Effect Estimation With Multi-Task Gaussian Processescited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Transformed Latent Variable Multi-Output Gaussian Processescited by 0