Gaussian Processes for Big Data
This work addresses the scalability bottleneck for Gaussian processes, making them practical for big data applications.
The authors tackled the problem of scaling Gaussian process models to large datasets by introducing stochastic variational inference, enabling application to millions of data points.
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 ap- proach 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.