# Doubly Stochastic Variational Inference for Deep Gaussian Processes

@inproceedings{Salimbeni2017DoublySV, title={Doubly Stochastic Variational Inference for Deep Gaussian Processes}, author={Hugh Salimbeni and Marc Peter Deisenroth}, booktitle={NIPS}, year={2017} }

Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational…

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