Uniform convergence for Gaussian kernel ridge regression

This paper establishes the first polynomial convergence rates for Gaussian kernel ridge regression (KRR) with a fixed hyperparameter in both the uniform and the $L^2$-norm. The uniform convergence result closes a gap in the theoretical understanding of KRR with the Gaussian kernel, where no such rates were previously known. In addition, we prove a polynomial $L^2$-convergence rate in the case, where the Gaussian kernel’s width parameter is fixed. This also contributes to the broader understanding of smooth kernels, for which previously only sub-polynomial $L^2$-rates were known in similar settings. Together, these results provide new theoretical justification for the use of Gaussian KRR with fixed hyperparameters in nonparametric regression.