Nicolas Durrande | DeepAI

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∙ 06/15/2021

Kernel Identification Through Transformers

Kernel selection plays a central role in determining the performance of ...

0 Fergus Simpson, et al. ∙

∙ 05/10/2021

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Deep Gaussian processes (DGPs) have struggled for relevance in applicati...

23 Vincent Dutordoir, et al. ∙

∙ 03/11/2021

The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain

In the univariate setting, using the kernel spectral representation is a...

0 Fergus Simpson, et al. ∙

∙ 12/27/2020

A Tutorial on Sparse Gaussian Processes and Variational Inference

Gaussian processes (GPs) provide a framework for Bayesian inference that...

0 Felix Leibfried, et al. ∙

∙ 10/29/2020

Matern Gaussian Processes on Graphs

Gaussian processes are a versatile framework for learning unknown functi...

17 Viacheslav Borovitskiy, et al. ∙

∙ 06/30/2020

Sparse Gaussian Processes with Spherical Harmonic Features

We introduce a new class of inter-domain variational Gaussian processes ...

17 Vincent Dutordoir, et al. ∙

∙ 06/25/2020

Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation

Many machine learning models require a training procedure based on runni...

0 Victor Picheny, et al. ∙

∙ 02/12/2020

Regret Bounds for Noise-Free Bayesian Optimization

Bayesian optimisation is a powerful method for non-convex black-box opti...

0 Sattar Vakili, et al. ∙

∙ 01/15/2020

Doubly Sparse Variational Gaussian Processes

The use of Gaussian process models is typically limited to datasets with...

27 Vincent Adam, et al. ∙

∙ 01/12/2020

Bayesian Quantile and Expectile Optimisation

Bayesian optimisation is widely used to optimise stochastic black box fu...

24 Léonard Torossian, et al. ∙

∙ 02/28/2019

Gaussian Process Modulated Cox Processes under Linear Inequality Constraints

Gaussian process (GP) modulated Cox processes are widely used to model p...

0 Andrés F. López-Lopera, et al. ∙

∙ 02/26/2019

Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era

Banded matrices can be used as precision matrices in several models incl...

10 Nicolas Durrande, et al. ∙

∙ 01/15/2019

Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC

Adding inequality constraints (e.g. boundedness, monotonicity, convexity...

0 Andrés F. López-Lopera, et al. ∙

∙ 12/28/2018

Scalable GAM using sparse variational Gaussian processes

Generalized additive models (GAMs) are a widely used class of models of ...

0 Vincent Adam, et al. ∙

∙ 08/29/2018

Physically-inspired Gaussian processes for transcriptional regulation in Drosophila melanogaster

The regulatory process in Drosophila melanogaster is thoroughly studied ...

12 Andrés F. López-Lopera, et al. ∙

∙ 10/20/2017

Finite-dimensional Gaussian approximation with linear inequality constraints

Introducing inequality constraints in Gaussian process (GP) models can l...

0 Andrés F. López-Lopera, et al. ∙

∙ 11/21/2016

Variational Fourier features for Gaussian processes

This work brings together two powerful concepts in Gaussian processes: t...

0 James Hensman, et al. ∙

∙ 07/19/2016

Nested Kriging predictions for datasets with large number of observations

This work falls within the context of predicting the value of a real fun...

0 Didier Rullière, et al. ∙

∙ 02/02/2016

An analytic comparison of regularization methods for Gaussian Processes

Gaussian Processes (GPs) are a popular approach to predict the output of...

0 Hossein Mohammadi, et al. ∙

∙ 08/06/2013

Invariances of random fields paths, with applications in Gaussian Process Regression

We study pathwise invariances of centred random fields that can be contr...

0 David Ginsbourger, et al. ∙

∙ 11/27/2011

Additive Covariance Kernels for High-Dimensional Gaussian Process Modeling

Gaussian process models -also called Kriging models- are often used as m...

0 Nicolas Durrande, et al. ∙

∙ 06/17/2011

ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis

Given a reproducing kernel Hilbert space H of real-valued functions and ...

0 Nicolas Durrande, et al. ∙

∙ 03/21/2011

Additive Kernels for Gaussian Process Modeling

Gaussian Process (GP) models are often used as mathematical approximatio...

0 Nicolas Durrande, et al. ∙