Gaussian Processes for Big Data


We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be vari-ationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform variational inference. Our approach is readily extended to models with non-Gaussian likelihoods and latent variable models based around Gaussian processes. We demonstrate the approach on a simple toy problem and two real world data sets.

Extracted Key Phrases

Showing 1-10 of 107 extracted citations
Citations per Year

144 Citations

Semantic Scholar estimates that this publication has received between 114 and 189 citations based on the available data.

See our FAQ for additional information.