Scalable GAM using sparse variational Gaussian processes
This work addresses scalability issues in Bayesian GAMs for statisticians, but it is incremental as it builds on existing variational inference techniques.
The authors tackled the challenge of scaling Bayesian generalized additive models (GAMs) by using sparse variational Gaussian processes, resulting in a method that is scalable and well-calibrated.
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.