  The composition of multiple Gaussian Processes as a Deep Gaussian Process
(DGP) enables a deep probabilistic nonparametric approach to flexibly tackle
complex machine learning problems with sound quantification of uncertainty.
Existing inference approaches for DGP models have limited scalability and are
notoriously cumbersome to construct. In this work, we introduce a novel
formulation of DGPs based on random feature expansions that we train using
stochastic variational inference. This yields a practical learning framework
which significantly advances the state-of-the-art in inference for DGPs, and
enables accurate quantification of uncertainty. We extensively showcase the
scalability and performance of our proposal on several datasets with up to 8
million observations, and various DGP architectures with up to 30 hidden
layers.
