# Asynchronous Distributed Variational Gaussian Processes for Regression

## TL;DR

This paper introduces ADVGP, an asynchronous distributed variational Gaussian process method that significantly improves scalability and efficiency for large-scale regression tasks involving billions of data points.

## Contribution

The paper presents the first asynchronous distributed variational GP inference framework, ADVGP, enabling scalable regression on massive datasets with improved efficiency and competitive accuracy.

## Key findings

- Scales GP regression to billions of samples.
- Achieves superior prediction accuracy over linear models.
- Maintains or improves predictive performance with higher efficiency.

## Abstract

Gaussian processes (GPs) are powerful non-parametric function estimators. However, their applications are largely limited by the expensive computational cost of the inference procedures. Existing stochastic or distributed synchronous variational inferences, although have alleviated this issue by scaling up GPs to millions of samples, are still far from satisfactory for real-world large applications, where the data sizes are often orders of magnitudes larger, say, billions. To solve this problem, we propose ADVGP, the first Asynchronous Distributed Variational Gaussian Process inference for regression, on the recent large-scale machine learning platform, PARAMETERSERVER. ADVGP uses a novel, flexible variational framework based on a weight space augmentation, and implements the highly efficient, asynchronous proximal gradient optimization. While maintaining comparable or better predictive performance, ADVGP greatly improves upon the efficiency of the existing variational methods. With ADVGP, we effortlessly scale up GP regression to a real-world application with billions of samples and demonstrate an excellent, superior prediction accuracy to the popular linear models.