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Manifold Gaussian Processes for Regression

Calandra, R; Peters, J; Rasmussen, CE; Deisenroth, MP; (2016) Manifold Gaussian Processes for Regression. In: Estevez, PA, (ed.) Proceedings of International Joint Conference on Neural Networks 2016 (IJCNN 2016). (pp. pp. 3338-3345). IEEE Xplore: New York, NY, USA. Green open access

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Abstract

Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness assumptions on the structure of the function to be modeled. To model complex and non-differentiable functions, these smoothness assumptions are often too restrictive. One way to alleviate this limitation is to find a different representation of the data by introducing a feature space. This feature space is often learned in an unsupervised way, which might lead to data representations that are not useful for the overall regression task. In this paper, we propose Manifold Gaussian Processes, a novel supervised method that jointly learns a transformation of the data into a feature space and a GP regression from the feature space to observed space. The Manifold GP is a full GP and allows to learn data representations, which are useful for the overall regression task. As a proof-of-concept, we evaluate our approach on complex non-smooth functions where standard GPs perform poorly, such as step functions and robotics tasks with contacts.

Type: Proceedings paper
Title: Manifold Gaussian Processes for Regression
Event: International Joint Conference on Neural Networks (IJCNN 2016), 24-29 July 2016, Vancouver, BC, Canada
Location: Vancouver, CANADA
Dates: 24 July 2016 - 29 July 2016
ISBN: 978-1-5090-0620-5
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/IJCNN.2016.7727626
Publisher version: https://doi.org/10.1109/IJCNN.2016.7727626
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.
Keywords: Standards, Gaussian processes, Manifolds, Bayes methods, Training, Computational modeling, Supervised learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10083570
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