BibTex format
@inproceedings{Deisenroth:2015,
author = {Deisenroth, MP and Ng, JW},
pages = {1481--1490},
title = {Distributed Gaussian processes},
url = {http://hdl.handle.net/10044/1/21163},
year = {2015}
}
RIS format (EndNote, RefMan)
TY - CPAPER
AB - 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.
AU - Deisenroth,MP
AU - Ng,JW
EP - 1490
PY - 2015///
SP - 1481
TI - Distributed Gaussian processes
UR - http://hdl.handle.net/10044/1/21163
ER -