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Poster

Doubly Stochastic Variational Inference for Deep Gaussian Processes

Hugh Salimbeni · Marc Deisenroth

Pacific Ballroom #195

Keywords: [ Variational Inference ] [ Gaussian Processes ] [ Hierarchical Models ]


Abstract:

Deep Gaussian processes (DGPs) are multi-layer generalizations 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 inference algorithm, which does not force independence between layers. With our method of inference we demonstrate that a DGP model can be used effectively on data ranging in size from hundreds to a billion points. We provide strong empirical evidence that our inference scheme for DGPs works well in practice in both classification and regression.

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