ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis. - Nicolas Durrande; David Ginsbourger; Olivier Roustant; Laurent Carraro (J. Multivar. Anal., 2013)

On ANOVA Decompositions of Kernels and Gaussian Random Field Paths. - David Ginsbourger; Olivier Roustant; Dominic Schuhmacher; Nicolas Durrande; Nicolas Lenz (MCQMC, 2014)

Detecting periodicities with Gaussian processes. - Nicolas Durrande; James Hensman; Magnus Rattray; Neil D. Lawrence (PeerJ Prepr., 2016)

Variational Fourier Features for Gaussian Processes. - James Hensman; Nicolas Durrande; Arno Solin (J. Mach. Learn. Res., 2017)

Physically-inspired Gaussian processes for transcriptional regulation in Drosophila melanogaster. - Andrés F. López-Lopera; Nicolas Durrande; Mauricio A. Álvarez (CoRR, 2018)

Scalable GAM using sparse variational Gaussian processes. - Vincent Adam; Nicolas Durrande; S. T. John (CoRR, 2018)

Finite-Dimensional Gaussian Approximation with Linear Inequality Constraints. - Andrés F. López-Lopera; François Bachoc; Nicolas Durrande; Olivier Roustant (SIAM/ASA J. Uncertain. Quantification, 2018)

Nested Kriging predictions for datasets with a large number of observations. - Didier Rullière; Nicolas Durrande; François Bachoc; Clément Chevalier (Stat. Comput., 2018)

Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era. - Nicolas Durrande; Vincent Adam; Lucas Bordeaux; Stefanos Eleftheriadis; James Hensman (AISTATS, 2019)

Gaussian Process Modulated Cox Processes under Linear Inequality Constraints. - Andrés F. López-Lopera; S. T. John; Nicolas Durrande (AISTATS, 2019)

Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC. - Andrés F. López-Lopera; François Bachoc; Nicolas Durrande; Olivier Roustant (CoRR, 2019)

Doubly Sparse Variational Gaussian Processes. - Vincent Adam; Stefanos Eleftheriadis; Artem Artemev; Nicolas Durrande; James Hensman (AISTATS, 2020)

Sparse Gaussian Processes with Spherical Harmonic Features. - Vincent Dutordoir; Nicolas Durrande; James Hensman (ICML, 2020)

Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation. - Victor Picheny; Vincent Dutordoir; Artem Artemev; Nicolas Durrande (ECML/PKDD (3), 2020)

Bayesian Quantile and Expectile Optimisation. - Léonard Torossian; Victor Picheny; Nicolas Durrande (CoRR, 2020)

Regret Bounds for Noise-Free Bayesian Optimization. - Sattar Vakili; Victor Picheny; Nicolas Durrande (CoRR, 2020)

A Tutorial on Sparse Gaussian Processes and Variational Inference. - Felix Leibfried; Vincent Dutordoir; S. T. John; Nicolas Durrande (CoRR, 2020)

Matérn Gaussian Processes on Graphs. - Viacheslav Borovitskiy; Iskander Azangulov; Alexander Terenin; Peter Mostowsky; Marc Peter Deisenroth; Nicolas Durrande (AISTATS, 2021)

The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain. - Fergus Simpson; Alexis Boukouvalas; Václav Cadek; Elvijs Sarkans; Nicolas Durrande (AISTATS, 2021)

Deep Neural Networks as Point Estimates for Deep Gaussian Processes. - Vincent Dutordoir; James Hensman; Mark van der Wilk; Carl Henrik Ek; Zoubin Ghahramani; Nicolas Durrande (NeurIPS, 2021)

Kernel Identification Through Transformers. - Fergus Simpson; Ian Davies; Vidhi Lalchand; Alessandro Vullo; Nicolas Durrande; Carl Edward Rasmussen (NeurIPS, 2021)

Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in Drosophila. - Andrés F. López-Lopera; Nicolas Durrande; Mauricio A. Álvarez (IEEE ACM Trans. Comput. Biol. Bioinform., 2021)

Bayesian quantile and expectile optimisation. - Victor Picheny; Henry B. Moss; Léeonard Torossian; Nicolas Durrande (UAI, 2022)