A1472
Title: Posterior contraction for deep Gaussian process priors
Authors: Gianluca Finocchio - University of Vienna (Austria) [presenting]
Johannes Schmidt-Hieber - University of Twente (Netherlands)
Abstract: Posterior contraction rates are studied for a class of deep Gaussian process priors applied to the nonparametric regression problem under a general composition assumption on the regression function. It is shown that the contraction rates can achieve the minimax convergence rate (up to log n factors) while being adaptive to the underlying structure and smoothness of the target function. The proposed framework extends the Bayesian nonparametric theory for Gaussian process priors.
