Nicolas Durrande

Publication Activity (10 Years)

Years Active: 2013-2022
Publications (10 Years): 32

Top Topics

Gaussian Process

Top Venues

Publications

Victor Picheny, Henry B. Moss, Léeonard Torossian, Nicolas Durrande
Bayesian quantile and expectile optimisation. UAI (2022)
Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande
Deep Neural Networks as Point Estimates for Deep Gaussian Processes. CoRR (2021)
Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, Nicolas Durrande
Matérn Gaussian Processes on Graphs. AISTATS (2021)
Fergus Simpson, Alexis Boukouvalas, Václav Cadek, Elvijs Sarkans, Nicolas Durrande
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain. CoRR (2021)
Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl Edward Rasmussen
Kernel Identification Through Transformers. NeurIPS (2021)
Fergus Simpson, Alexis Boukouvalas, Václav Cadek, Elvijs Sarkans, Nicolas Durrande
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain. AISTATS (2021)
Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl E. Rasmussen
Kernel Identification Through Transformers. CoRR (2021)
Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande
Deep Neural Networks as Point Estimates for Deep Gaussian Processes. NeurIPS (2021)
Andrés F. López-Lopera, Nicolas Durrande, Mauricio A. Álvarez
Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in Drosophila. IEEE ACM Trans. Comput. Biol. Bioinform. 18 (2) (2021)
Sattar Vakili, Victor Picheny, Nicolas Durrande
Regret Bounds for Noise-Free Bayesian Optimization. CoRR (2020)
Léonard Torossian, Victor Picheny, Nicolas Durrande
Bayesian Quantile and Expectile Optimisation. CoRR (2020)
Vincent Adam, Stefanos Eleftheriadis, Artem Artemev, Nicolas Durrande, James Hensman
Doubly Sparse Variational Gaussian Processes. AISTATS (2020)
Vincent Adam, Stefanos Eleftheriadis, Nicolas Durrande, Artem Artemev, James Hensman
Doubly Sparse Variational Gaussian Processes. CoRR (2020)
Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, Nicolas Durrande
Matern Gaussian Processes on Graphs. CoRR (2020)
Victor Picheny, Vincent Dutordoir, Artem Artemev, Nicolas Durrande
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation. CoRR (2020)
Felix Leibfried, Vincent Dutordoir, S. T. John, Nicolas Durrande
A Tutorial on Sparse Gaussian Processes and Variational Inference. CoRR (2020)
Victor Picheny, Vincent Dutordoir, Artem Artemev, Nicolas Durrande
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation. ECML/PKDD (3) (2020)
Vincent Dutordoir, Nicolas Durrande, James Hensman
Sparse Gaussian Processes with Spherical Harmonic Features. CoRR (2020)
Vincent Dutordoir, Nicolas Durrande, James Hensman
Sparse Gaussian Processes with Spherical Harmonic Features. ICML (2020)
Andrés F. López-Lopera, S. T. John, Nicolas Durrande
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints. CoRR (2019)
Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era. AISTATS (2019)
Andrés F. López-Lopera, S. T. John, Nicolas Durrande
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints. AISTATS (2019)
Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era. CoRR (2019)
Andrés F. López-Lopera, François Bachoc, Nicolas Durrande, Olivier Roustant
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC. CoRR (2019)
Andrés F. López-Lopera, Nicolas Durrande, Mauricio A. Álvarez
Physically-inspired Gaussian processes for transcriptional regulation in Drosophila melanogaster. CoRR (2018)
Andrés F. López-Lopera, François Bachoc, Nicolas Durrande, Olivier Roustant
Finite-Dimensional Gaussian Approximation with Linear Inequality Constraints. SIAM/ASA J. Uncertain. Quantification 6 (3) (2018)
Didier Rullière, Nicolas Durrande, François Bachoc, Clément Chevalier
Nested Kriging predictions for datasets with a large number of observations. Stat. Comput. 28 (4) (2018)
Vincent Adam, Nicolas Durrande, S. T. John
Scalable GAM using sparse variational Gaussian processes. CoRR (2018)
Andrés F. López-Lopera, François Bachoc, Nicolas Durrande, Olivier Roustant
Finite-dimensional Gaussian approximation with linear inequality constraints. CoRR (2017)
James Hensman, Nicolas Durrande, Arno Solin
Variational Fourier Features for Gaussian Processes. J. Mach. Learn. Res. 18 (2017)
Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence
Detecting periodicities with Gaussian processes. PeerJ Comput. Sci. 2 (2016)
Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence
Detecting periodicities with Gaussian processes. PeerJ Prepr. 4 (2016)
David Ginsbourger, Olivier Roustant, Dominic Schuhmacher, Nicolas Durrande, Nicolas Lenz
On ANOVA Decompositions of Kernels and Gaussian Random Field Paths. MCQMC (2014)
Nicolas Durrande, David Ginsbourger, Olivier Roustant, Laurent Carraro
ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis. J. Multivar. Anal. 115 (2013)