We study the learning properties of nonparametric minimum norm interpolating estimators. In particular, we consider estimators defined by so called Matern kernels, and focus on the role of the kernels scale and smoothness. While common ML wisdom suggests estimators defined by large function classes might be prone to overfit the data, here we suggest that they can often be more stable.
Our analysis uses a mix of results from interpolation theory and probability theory. Extensive numerical results are provided to investigate the usefulness of the obtained learning bounds.