Asynchronous Distributed Variational Gaussian Processes for Regression
This work addresses the scalability problem for practitioners needing Gaussian processes on massive real-world datasets, though it is incremental as it builds on existing variational methods.
The paper tackles the computational bottleneck of Gaussian process regression for large datasets by proposing ADVGP, an asynchronous distributed variational inference method that scales to billions of samples and achieves superior prediction accuracy compared to linear models.
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.