# Scalable GAM using sparse variational Gaussian processes

## TL;DR

This paper introduces a scalable Bayesian approach to generalized additive models using sparse variational Gaussian processes, enabling efficient and interpretable modeling of complex data.

## Contribution

It presents a novel scalable Bayesian GAM framework with sparse GPs and variational inference, improving computational efficiency and model calibration.

## Key findings

- Efficient inference for large datasets using sparse GPs.
- Enhanced interpretability of GAM components.
- Well-calibrated Bayesian uncertainty estimates.

## Abstract

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.