Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations

Balázs Csanád Csáji, Krisztián Balázs Kis

We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributional assumptions, hence, instead…