Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression

TL;DR

This paper introduces a scalable hierarchical mixture-of-experts Gaussian process model capable of handling large-scale nonlinear regression tasks efficiently through distributed computation, surpassing previous limitations in data size.

Contribution

The paper presents a novel hierarchical mixture-of-experts model that enables large-scale Gaussian process regression without sparse approximations, utilizing closed-form distributed computations.

Findings

Handles datasets with millions of points

Efficient parallelization with low memory use

Demonstrates applicability to large real-world data

Abstract

We propose a practical and scalable Gaussian process model for large-scale nonlinear probabilistic regression. Our mixture-of-experts model is conceptually simple and hierarchically recombines computations for an overall approximation of a full Gaussian process. Closed-form and distributed computations allow for efficient and massive parallelisation while keeping the memory consumption small. Given sufficient computing resources, our model can handle arbitrarily large data sets, without explicit sparse approximations. We provide strong experimental evidence that our model can be applied to large data sets of sizes far beyond millions. Hence, our model has the potential to lay the foundation for general large-scale Gaussian process research.