Additive Gaussian Processes

TL;DR

This paper presents Additive Gaussian Processes, a flexible model that decomposes functions into sums of low-dimensional components, improving interpretability and predictive accuracy in regression tasks.

Contribution

It introduces a novel additive GP model with an efficient kernel parameterization, enabling scalable learning of input interactions and broadening the applicability of GPs.

Findings

Achieves state-of-the-art predictive performance in regression.

Enhances interpretability through additive structure.

Provides an efficient method for evaluating input interaction terms.

Abstract

We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-exponential kernels. Hyperparameter learning in this model can be seen as Bayesian Hierarchical Kernel Learning (HKL). We introduce an expressive but tractable parameterization of the kernel function, which allows efficient evaluation of all input interaction terms, whose number is exponential in the input dimension. The additional structure discoverable by this model results in increased interpretability, as well as state-of-the-art predictive power in regression tasks.