arXiv stat.ML
· Papers
Adaptive Nystr"om for Gaussian Process Regression
arXiv:2607.27427v1 Announce Type: cross Abstract: Gaussian Process Regression (GPR) is a robust framework for uncertainty quantification, yet its $O(n^3)$ complexity limits its scalability. Low-rank Nystr"om approximations can reduce this burden to $O(nm^2)$, but their accuracy depends heavily on the selection of land