Sebastian Schulze

Marginalising over Stationary Kernels with Bayesian Quadrature

2021 · arXiv (Cornell University)

Marginalising over families of Gaussian Process kernels produces flexible model classes with well-calibrated uncertainty estimates. Existing approaches require likelihood evaluations of many kernels, rendering them prohibitively expensive for larger datasets. We propose a Bayesian Quadrature …