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- James Hensman, Nicolas Durrande, Arno Solin
- Journal of Machine Learning Research
- 2017

This work brings together two powerful concepts in Gaussian processes: the variational approach to sparse approximation and the spectral representation of Gaussian processes. This gives rise to anâ€¦ (More)

- Didier RulliÃ¨re, Nicolas Durrande, FranÃ§ois Bachoc, ClÃ©ment Chevalier
- Statistics and Computing
- 2018

This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function. The Kriging interpolation technique (orâ€¦ (More)

- Nicolas Durrande, David Ginsbourger, Olivier Roustant, Laurent Carraro
- J. Multivariate Analysis
- 2013

Given a reproducing kernel Hilbert space (H, ã€ˆ., .ã€‰) of real-valued functions and a suitable measure Î¼ over the source space D âŠ‚ R, we decompose H as the sum of a subspace of centered functions for Î¼â€¦ (More)

- David Ginsbourger, Bastien Rosspopoff, Guillaume Pirot, Nicolas Durrande, Philippe Re
- 2012

â‡‘ Corresponding author. E-mail addresses: david.ginsbourger@stat.unibe.ch (D. Ginsbourger), bastien. rosspopoff@mines-saint-etienne.fr (B. Rosspopoff), guillaume.pirot@unine.ch (G. Pirot),â€¦ (More)

Gaussian Process (GP) models are often used as mathematical approximations of time expensive numerical simulators. Provided that its kernel is suitably chosen and that enough data is available toâ€¦ (More)

We study pathwise invariances and degeneracies of random fields with motivating applications in Gaussian process modelling. The key idea is that a number of structural properties one may wish toâ€¦ (More)

Gaussian Process (GP) models are often used as mathematical approximations of computationally expensive experiments. Provided that its kernel is suitably chosen and that enough data is available toâ€¦ (More)

- Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence
- PeerJ Computer Science
- 2016

We consider the problem of detecting and quantifying the periodic component of a function given noise-corrupted observations of a limited number of input/output tuples. Our approach is based onâ€¦ (More)

Gaussian Processes (GPs) are often used to predict the output of a parameterized deterministic experiment. They have many applications in the field of Computer Experiments, in particular to performâ€¦ (More)

Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems. We consider the finite-dimensionalâ€¦ (More)