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07/11/2023
Assessing the calibration of multivariate probabilistic forecasts
Rank and PIT histograms are established tools to assess the calibration ...
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07/27/2022
Disintegration of Gaussian Measures for Sequential Assimilation of Linear Operator Data
Gaussian processes appear as building blocks in various stochastic model...
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06/15/2022
Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures
We present new classes of positive definite kernels on non-standard spac...
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02/25/2022
Evaluating forecasts for high-impact events using transformed kernel scores
It is informative to evaluate a forecaster's ability to predict outcomes...
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10/11/2021
Lithological Tomography with the Correlated Pseudo-Marginal Method
We consider lithological tomography in which the posterior distribution ...
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10/06/2021
Continuous logistic Gaussian random measure fields for spatial distributional modelling
We investigate a class of models for non-parametric estimation of probab...
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09/08/2021
Uncertainty Quantification and Experimental Design for large-scale linear Inverse Problems under Gaussian Process Priors
We consider the use of Gaussian process (GP) priors for solving inverse ...
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04/16/2021
Fast ABC with joint generative modelling and subset simulation
We propose a novel approach for solving inverse-problems with high-dimen...
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02/15/2021
Goal-oriented adaptive sampling under random field modelling of response probability distributions
In the study of natural and artificial complex systems, responses that a...
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01/08/2021
Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariances
We generalize fast Gaussian process leave-one-out formulae to multiple-f...
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07/07/2020
Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling
Improving and optimizing oceanographic sampling is a crucial task for ma...
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12/26/2019
Area-covering postprocessing of ensemble precipitation forecasts using topographical and seasonal conditions
Probabilistic weather forecasts from ensemble systems require statistica...
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10/11/2019
Learning from demonstration with model-based Gaussian process
In learning from demonstrations, it is often desirable to adapt the beha...
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10/09/2019
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization
We focus on kernel methods for set-valued inputs and their application t...
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11/06/2017
Modelling non-stationary extreme precipitation with max-stable processes and multi-dimensional scaling
Modeling the joint distribution of extreme weather events in several loc...
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04/18/2017
On the choice of the low-dimensional domain for global optimization via random embeddings
The challenge of taking many variables into account in optimization prob...
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09/09/2016
Efficient batch-sequential Bayesian optimization with moments of truncated Gaussian vectors
We deal with the efficient parallelization of Bayesian global optimizati...
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08/03/2016
A supermartingale approach to Gaussian process based sequential design of experiments
Gaussian process (GP) models have become a well-established frameworkfor...
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03/18/2015
Differentiating the multipoint Expected Improvement for optimal batch design
This work deals with parallel optimization of expensive objective functi...
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01/15/2015
Quantifying uncertainties on excursion sets under a Gaussian random field prior
We focus on the problem of estimating and quantifying uncertainties on t...
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11/13/2014
A warped kernel improving robustness in Bayesian optimization via random embeddings
This works extends the Random Embedding Bayesian Optimization approach b...
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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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03/29/2012
Corrected Kriging update formulae for batch-sequential data assimilation
Recently, a lot of effort has been paid to the efficient computation of ...
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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