Deisenroth, MP;
Ng, JW;
(2015)
Distributed Gaussian processes.
In:
ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine.
(pp. pp. 1481-1490).
ACM: Lille, France.
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Abstract
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-theart 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, recombine 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.
| Type: | Proceedings paper |
|---|---|
| Title: | Distributed Gaussian processes |
| Event: | 32nd International Conference on International Conference on Machine |
| ISBN-13: | 9781510810587 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://dl.acm.org/doi/abs/10.5555/3045118.3045276 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10083558 |
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