Skip to Main content Skip to Navigation
Journal articles

Approximate inference in related multi-output Gaussian Process Regression

Abstract : 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.
Document type :
Journal articles
Complete list of metadatas

Cited literature [16 references]  Display  Hide  Download
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Tuesday, July 3, 2018 - 12:42:04 PM
Last modification on : Wednesday, June 24, 2020 - 4:18:55 PM
Long-term archiving on: : Monday, October 1, 2018 - 12:00:59 PM


Files produced by the author(s)



Ankit Chiplunkar, Emmanuel Rachelson, Michele Colombo, Joseph Morlier. Approximate inference in related multi-output Gaussian Process Regression. Lecture Notes in Computer Science, Springer, 2017, pp.88-103. ⟨10.1007/978-3-319-53375-9_5⟩. ⟨hal-01828689⟩



Record views


Files downloads