Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes

arXiv:2308.14142v2 Announce Type: replace-cross
Abstract: Sparse variational approximations are popular methods for scaling up inference and learning in Gaussian processes to larger datasets. For $N$ training points, exact inference has $O(N^3)$ cost; with $M \ll N$ features, state of the art sparse variational methods have $O(NM^2)$ cost. Recently, methods have been proposed using more sophisticated features; these promise $O(M^3)$ cost, with good performance in low dimensional tasks such as spatial modelling, but they only work with a very limited class …

abstract art arxiv cost cs.lg datasets features fourier gaussian processes inference modelling popular processes scaling scaling up spatial state state of the art stat.ml training type