Machine learning for Science

We consider the problem of learning a linear operator between Sobolev RKHSs from noisy data. Different from its finite-dimensional counterpart where regularized least squares is optimal, we prove that estimators with a certain multilevel structure is necessary (and sufficient) to achieve optimality.