Robust and Conjugate Spatio-Temporal Gaussian Processes
Reviewed by Pithpith:ESZR3T4Qopen to challenge →
read the original abstract
State-space formulations allow for Gaussian process (GP) regression with linear-in-time computational cost in spatio-temporal settings, but performance typically suffers in the presence of outliers. In this paper, we adapt and specialise the robust and conjugate GP (RCGP) framework of Altamirano et al. (2024) to the spatio-temporal setting. In doing so, we obtain an outlier-robust spatio-temporal GP with a computational cost comparable to classical spatio-temporal GPs. We also overcome the three main drawbacks of RCGPs: their unreliable performance when the prior mean is chosen poorly, their lack of reliable uncertainty quantification, and the need to carefully select a hyperparameter by hand. We study our method extensively in finance and weather forecasting applications, demonstrating that it provides a reliable approach to spatio-temporal modelling in the presence of outliers.
This paper has not been read by Pith yet.
Forward citations
Cited by 3 Pith papers
-
BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
BALLAST combines Bayesian active learning with trajectory look-ahead to optimize sequential placement of sea-drifters for inferring spatio-temporal vector fields using physics-informed GPs, with demonstrated benefits ...
-
BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
Introduces BALLAST, a Bayesian active learning framework with look-ahead amendment for optimizing Lagrangian observer placement to infer spatio-temporal vector fields via physics-informed GPs, with benefits shown on s...
-
Concentration and Calibration in Predictive Bayesian Inference
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.