Rafael Savvides

Error bounds for any regression model using Gaussian processes with gradient information

We provide an upper bound for the expected quadratic loss on new data for any regression model. We derive the bound by modelling the underlying function by a Gaussian process (GP). Instead of a single kernel or family of kernels of the same form, we consider all GPs with translation-invariant and co…