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06/15/2021
Kernel Identification Through Transformers
Kernel selection plays a central role in determining the performance of ...
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05/10/2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Deep Gaussian processes (DGPs) have struggled for relevance in applicati...
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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...
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12/27/2020
A Tutorial on Sparse Gaussian Processes and Variational Inference
Gaussian processes (GPs) provide a framework for Bayesian inference that...
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10/29/2020
Matern Gaussian Processes on Graphs
Gaussian processes are a versatile framework for learning unknown functi...
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06/30/2020
Sparse Gaussian Processes with Spherical Harmonic Features
We introduce a new class of inter-domain variational Gaussian processes ...
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06/25/2020
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation
Many machine learning models require a training procedure based on runni...
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02/12/2020
Regret Bounds for Noise-Free Bayesian Optimization
Bayesian optimisation is a powerful method for non-convex black-box opti...
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01/15/2020
Doubly Sparse Variational Gaussian Processes
The use of Gaussian process models is typically limited to datasets with...
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01/12/2020
Bayesian Quantile and Expectile Optimisation
Bayesian optimisation is widely used to optimise stochastic black box fu...
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02/28/2019
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints
Gaussian process (GP) modulated Cox processes are widely used to model p...
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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...
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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...
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12/28/2018
Scalable GAM using sparse variational Gaussian processes
Generalized additive models (GAMs) are a widely used class of models of ...
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08/29/2018
Physically-inspired Gaussian processes for transcriptional regulation in Drosophila melanogaster
The regulatory process in Drosophila melanogaster is thoroughly studied ...
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10/20/2017
Finite-dimensional Gaussian approximation with linear inequality constraints
Introducing inequality constraints in Gaussian process (GP) models can l...
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11/21/2016
Variational Fourier features for Gaussian processes
This work brings together two powerful concepts in Gaussian processes: t...
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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...
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02/02/2016
An analytic comparison of regularization methods for Gaussian Processes
Gaussian Processes (GPs) are a popular approach to predict the output of...
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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...
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11/27/2011
Additive Covariance Kernels for High-Dimensional Gaussian Process Modeling
Gaussian process models -also called Kriging models- are often used as m...
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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 ...
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03/21/2011