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Matern Gaussian Processes on Graphs

Borovitskiy, V; Azangulov, I; Terenin, A; Mostowsky, P; Deisenroth, MP; Durrande, N; (2020) Matern Gaussian Processes on Graphs. arXiv: Ithaca, NY, USA. Green open access

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Abstract

Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information about their properties. Although many different Gaussian process models are readily available when the input space is Euclidean, the choice is much more limited for Gaussian processes whose input space is an undirected graph. In this work, we leverage the stochastic partial differential equation characterization of Mat´ern Gaussian processes—a widelyused model class in the Euclidean setting—to study their analog for undirected graphs. We show that the resulting Gaussian processes inherit various attractive properties of their Euclidean and Riemannian analogs and provide techniques that allow them to be trained using standard methods, such as inducing points. This enables graph Mat´ern Gaussian processes to be employed in mini-batch and non-conjugate settings, thereby making them more accessible to practitioners and easier to deploy within larger learning frameworks.

Type: Working / discussion paper
Title: Matern Gaussian Processes on Graphs
Open access status: An open access version is available from UCL Discovery
Publisher version: https://arxiv.org/abs/2010.15538v1
Language: English
Additional information: This version is the version of record. 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/10117309
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