Nicolas Durrande

Google DeepMind (United Kingdom) — United Kingdom

Computer Science · Physical Sciences

13h-index682citations75works0.02yr avg
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Research Topics

Gaussian Processes and Bayesian Inference(47), Advanced Multi-Objective Optimization Algorithms(34), Probabilistic and Robust Engineering Design(13), Control Systems and Identification(7), Optimal Experimental Design Methods(7)

Publications75 total

A discussion of the current and potential uses of Gaussian Processes in mechanics
SPIRE - Sciences Po Institutional REpository·2022
Properties and Comparison of Some Kriging Sub-model Aggregation Methods
Mathematical Geosciences·2022· 6 cited
Bayesian inversion of a diffusion model with application to biology
Journal of Mathematical Biology·2021Open Access
Kernel Identification Through Transformers
arXiv (Cornell University)·2021Open Access
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
arXiv (Cornell University)·2021· 1 citedOpen Access
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain.
International Conference on Artificial Intelligence and Statistics·2021· 1 cited
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain
arXiv (Cornell University)·2021Open Access
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation
Lecture notes in computer science·2021· 1 citedOpen Access
A Tutorial on Sparse Gaussian Processes and Variational Inference
arXiv (Cornell University)·2020· 12 citedOpen Access
Matérn Gaussian Processes on Graphs
arXiv (Cornell University)·2020· 2 citedOpen Access
Sparse Gaussian Processes with Spherical Harmonic Features
arXiv (Cornell University)·2020· 16 citedOpen Access
Automatic Tuning of Stochastic Gradient Descent with Bayesian\n Optimisation
arXiv (Cornell University)·2020Open Access
Doubly Sparse Variational Gaussian Processes
International Conference on Artificial Intelligence and Statistics·2020· 9 cited
Regret Bounds for Noise-Free Bayesian Optimization.
arXiv (Cornell University)·2020· 7 citedOpen Access
Doubly Sparse Variational Gaussian Processes
arXiv (Cornell University)·2020· 2 citedOpen Access
Bayesian Quantile and Expectile Optimisation.
arXiv (Cornell University)·2020· 2 citedOpen Access
Bayesian Quantile and Expectile Optimisation
arXiv (Cornell University)·2020· 3 citedOpen Access
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC
Springer proceedings in mathematics & statistics·2020· 1 citedOpen Access
Multi-Fidelity for MDO Using Gaussian Processes
Springer optimization and its applications·2020· 4 cited
An overview of kriging for researchers
2019Open Access
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Frequent Co-authors

Olivier Roustant(16), James Hensman(13), David Ginsbourger(13), Andrés F. López-Lopera(8), François Bachoc(8), Didier Rullière(4), Clément Chevalier(4), Magnus Rattray(4), Neil D. Lawrence(4), Rodolphe Le Riche(4), Vincent Dutordoir(4), Vincent Adam(4), Stefanos Eleftheriadis(4), Victor Picheny(4), St. John(3), Mauricio A. Álvarez(3), Artem Artemev(3), Laurent Carraro(2), N. Corrado(2), Marco Gherlone(2)