Random Feature Expansions for Deep Gaussian Processes

Understand

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.