Graph Random Features for Scalable Gaussian Processes
Machine Learning
2025-09-29 v2
Abstract
We study the application of graph random features (GRFs) - a recently introduced stochastic estimator of graph node kernels - to scalable Gaussian processes on discrete input spaces. We prove that (under mild assumptions) Bayesian inference with GRFs enjoys time complexity with respect to the number of nodes , compared to for exact kernels. Substantial wall-clock speedups and memory savings unlock Bayesian optimisation on graphs with over nodes on a single computer chip, whilst preserving competitive performance.
Cite
@article{arxiv.2509.03691,
title = {Graph Random Features for Scalable Gaussian Processes},
author = {Matthew Zhang and Jihao Andreas Lin and Krzysztof Choromanski and Adrian Weller and Richard E. Turner and Isaac Reid},
journal= {arXiv preprint arXiv:2509.03691},
year = {2025}
}