Scalable Gaussian Processes, with Guarantees: Kernel Approximations and Deep Feature Extraction

We provide a linear time inferential framework for Gaussian processes that supports automatic feature extraction through deep neural networks and low-rank kernel approximations. Importantly, we derive approximation guarantees bounding the Kullback–Leibler divergence between the idealized Gaussian process and one resulting from a low-rank ...More