Posterior Contraction for Deep Gaussian Process Priors
Gianluca Finocchio, Johannes Schmidt-Hieber; 24(66):1−49, 2023.
We study posterior contraction rates for a class of deep Gaussian process priors in the nonparametric regression setting 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.
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