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

Petros Dellaportas
Petros Dellaportas
Aristeidis Panos
Aristeidis Panos

arXiv preprint arXiv:2004.01584, 2020.

Cited by: 2|Views4

Abstract:

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

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