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Approximate inference in related multi-output Gaussian Process Regression

Chiplunkar, Ankit and Rachelson, Emmanuel and Colombo, Michele and Morlier, Joseph Approximate inference in related multi-output Gaussian Process Regression. (2017) Lecture Notes in Computer Science. 88-103. ISSN 0302-9743

(Document in English)

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Official URL: https://doi.org/10.1007/978-3-319-53375-9_5


In Gaussian Processes a multi-output kernel is a covariance function over correlated outputs. Using a prior known relation between outputs, joint auto- and cross-covariance functions can be constructed. Realizations from these joint-covariance functions give outputs that are consistent with the prior relation. One issue with gaussian process regression is efficient inference when scaling upto large datasets. In this paper we use approximate inference techniques upon multi-output kernels enforcing relationships between outputs. Results of the proposed methodology for theoretical data and real world applications are presented. The main contribution of this paper is the application and validation of our methodology on a dataset of real aircraft fight tests, while imposing knowledge of aircraft physics into the model.

Item Type:Article
HAL Id:hal-01828689
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Other partners > Airbus (FRANCE)
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Laboratory name:
Deposited On:27 Jul 2017 16:12

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