Nicolas Durrande
Nicolas Durrande
Principal Scientist at Monumo
Verified email at - Homepage
Cited by
Cited by
Variational Fourier features for Gaussian processes
J Hensman, N Durrande, A Solin
Journal of Machine Learning Research 18 (151), 1-52, 2018
Additive covariance kernels for high-dimensional Gaussian process modeling
N Durrande, D Ginsbourger, O Roustant
Annales de la Faculté des sciences de Toulouse: Mathématiques 21 (3), 481-499, 2012
ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis
N Durrande, D Ginsbourger, O Roustant, L Carraro
Journal of Multivariate Analysis 115, 57-67, 2013
Finite-dimensional Gaussian approximation with linear inequality constraints
AF López-Lopera, F Bachoc, N Durrande, O Roustant
SIAM/ASA Journal on Uncertainty Quantification 6 (3), 1224-1255, 2018
Nested Kriging predictions for datasets with a large number of observations
D Rullière, N Durrande, F Bachoc, C Chevalier
Statistics and Computing 28, 849-867, 2018
Matérn Gaussian processes on graphs
V Borovitskiy, I Azangulov, A Terenin, P Mostowsky, M Deisenroth, ...
International Conference on Artificial Intelligence and Statistics, 2593-2601, 2021
Sparse Gaussian processes with spherical harmonic features
V Dutordoir, N Durrande, J Hensman
International Conference on Machine Learning, 2793-2802, 2020
Detecting periodicities with Gaussian processes
N Durrande, J Hensman, M Rattray, ND Lawrence
PeerJ Computer Science 2, e50, 2016
A tutorial on sparse Gaussian processes and variational inference
F Leibfried, V Dutordoir, ST John, N Durrande
arXiv preprint arXiv:2012.13962, 2020
Distance-based kriging relying on proxy simulations for inverse conditioning
D Ginsbourger, B Rosspopoff, G Pirot, N Durrande, P Renard
Advances in water resources 52, 275-291, 2013
An analytic comparison of regularization methods for Gaussian processes
H Mohammadi, RL Riche, N Durrande, E Touboul, X Bay
arXiv preprint arXiv:1602.00853, 2016
Banded matrix operators for Gaussian Markov models in the automatic differentiation era
N Durrande, V Adam, L Bordeaux, S Eleftheriadis, J Hensman
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Deep neural networks as point estimates for deep Gaussian processes
V Dutordoir, J Hensman, M van der Wilk, CH Ek, Z Ghahramani, ...
Advances in Neural Information Processing Systems 34, 9443-9455, 2021
Doubly sparse variational Gaussian processes
V Adam, S Eleftheriadis, A Artemev, N Durrande, J Hensman
International Conference on Artificial Intelligence and Statistics, 2874-2884, 2020
On degeneracy and invariances of random fields paths with applications in Gaussian process modelling
D Ginsbourger, O Roustant, N Durrande
Journal of statistical planning and inference 170, 117-128, 2016
On ANOVA decompositions of kernels and Gaussian random field paths
D Ginsbourger, O Roustant, D Schuhmacher, N Durrande, N Lenz
Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April …, 2016
Bayesian quantile and expectile optimisation
V Picheny, H Moss, L Torossian, N Durrande
Uncertainty in Artificial Intelligence, 1623-1633, 2022
kergp: Gaussian process laboratory
Y Deville, D Ginsbourger, O Roustant, N Durrande
R package version 0.2. 0, 2015
Single and multiple crack localization in beam-like structures using a Gaussian process regression approach
N Corrado, N Durrande, M Gherlone, J Hensman, M Mattone, C Surace
Journal of Vibration and Control 24 (18), 4160-4175, 2018
Kernels and designs for modelling invariant functions: From group invariance to additivity
D Ginsbourger, N Durrande, O Roustant
mODa 10–Advances in Model-Oriented Design and Analysis: Proceedings of the …, 2013
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