Distributed Gaussian Processes
File(s)
Working paper
Author(s)
Deisenroth, MP
Ng, JW
Type
Report
Abstract
Copyright © 2015 by the author(s).To scale Gaussian processes (GPs) to large data sets we introduce the robust Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for large-scale distributed GP regression. Unlike state-of-the-art sparse GP approximations, the rBCM is conceptually simple and does not rely on inducing or variational parameters. The key idea is to recursively distribute computations to independent computational units and, subsequently, re-combine them to form an overall result. Efficient closed-form inference allows for straightforward parallelisation and distributed computations with a small memory footprint. The rBCM is independent of the computational graph and can be used on heterogeneous computing infrastructures, ranging from laptops to clusters. With sufficient computing resources our distributed GP model can handle arbitrarily large data sets.
Date Issued
2015-12-31
Copyright Statement
© 2015 The Authors
Description
08.04.15 KB. Ok to add working paper to spiral
Identifier
http://arxiv.org/abs/1502.02843v1
Notes
11 pages, 5 figures. arXiv admin note: text overlap with arXiv:1412.3078