Nicolas Durrande

34 papers A* 3A 6Journal 23Unranked 2

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