  Generalized additive models (GAMs) are a widely used class of models of
interest to statisticians as they provide a flexible way to design
interpretable models of data beyond linear models. We here propose a scalable
and well-calibrated Bayesian treatment of GAMs using Gaussian processes (GPs)
and leveraging recent advances in variational inference. We use sparse GPs to
represent each component and exploit the additive structure of the model to
efficiently represent a Gaussian a posteriori coupling between the components.

Machine Learning;Machine Learning