Posterior Contraction for Deep Gaussian Process Priors
Abstract
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.
Cite
Text
Finocchio and Schmidt-Hieber. "Posterior Contraction for Deep Gaussian Process Priors." Journal of Machine Learning Research, 2023.Markdown
[Finocchio and Schmidt-Hieber. "Posterior Contraction for Deep Gaussian Process Priors." Journal of Machine Learning Research, 2023.](https://mlanthology.org/jmlr/2023/finocchio2023jmlr-posterior/)BibTeX
@article{finocchio2023jmlr-posterior,
title = {{Posterior Contraction for Deep Gaussian Process Priors}},
author = {Finocchio, Gianluca and Schmidt-Hieber, Johannes},
journal = {Journal of Machine Learning Research},
year = {2023},
pages = {1-49},
volume = {24},
url = {https://mlanthology.org/jmlr/2023/finocchio2023jmlr-posterior/}
}