| Variational Fourier Features for Gaussian Processes. J Hensman, N Durrande, A Solin J. Mach. Learn. Res. 18 (1), 5537-5588, 2017 | 190 | 2017 |
| 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 | 134* | 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 | 92 | 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 | 73 | 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 | 69 | 2018 |
| Detecting periodicities with Gaussian processes N Durrande, J Hensman, M Rattray, ND Lawrence PeerJ Computer Science 2, e50, 2016 | 53* | 2016 |
| 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 | 43 | 2021 |
| Sparse Gaussian processes with spherical harmonic features V Dutordoir, N Durrande, J Hensman International Conference on Machine Learning, 2793-2802, 2020 | 43 | 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 | 42 | 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 | 34 | 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 | 33 | 2019 |
| A tutorial on sparse Gaussian processes and variational inference F Leibfried, V Dutordoir, ST John, N Durrande arXiv preprint arXiv:2012.13962, 2020 | 28 | 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 | 24 | 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 | 24 | 2016 |
| 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 | 20 | 2021 |
| Bayesian quantile and expectile optimisation V Picheny, H Moss, L Torossian, N Durrande Uncertainty in Artificial Intelligence, 1623-1633, 2022 | 19 | 2022 |
| 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 | 19 | 2020 |
| 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 | 17 | 2013 |
| Etude de classes de noyaux adaptées à la simplification et à l’interprétation des modeles d’approximation. Une approche fonctionnelle et probabiliste. N Durrande Ph. D. thesis, Saint-Etienne, EMSE, 2011 | 17 | 2011 |
| Gaussian process modulated Cox processes under linear inequality constraints AF López-Lopera, ST John, N Durrande The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 16 | 2019 |
James HensmanMicrosoft ResearchVerified email at microsoft.com
