Covariance-Boosted Gaussian Processes for Spatiotemporal Irregularities
Nonstationary Gaussian process (GP) models are powerful tools for capturing input-dependent variability by adapting to observed data. However, with limited sampling and highly parameterized covariance structure, they are often prone to overfitting and overconfident uncertainty estimates, potentially leading to misleading predictions in safety-critical applications. Motivated by ionospheric modeling for satellite-based augmentation systems (SBAS), this paper proposes a Covariance-Boosted Gaussian Process (CBGP) framework centered upon boosting covariance priors to discover nonstationary latent functions for signal and observation variation that capture irregularities in the input domain. A...
| Authors | Jeremy Ovadia |
|---|---|
| Primary category | stat.ML |
| Published | 25 July 2026 |
| Last updated at source | 25 August 2026 |
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