  We establish generic uniform convergence guarantees for Gaussian data in
terms of the Rademacher complexity of the hypothesis class and the Lipschitz
constant of the square root of the scalar loss function. We show how these
guarantees substantially generalize previous results based on smoothness
(Lipschitz constant of the derivative), and allow us to handle the broader
class of square-root-Lipschitz losses, which includes also non-smooth loss
functions appropriate for studying phase retrieval and ReLU regression, as well
as rederive and better understand "optimistic rate" and interpolation learning
guarantees.
