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07/29/2022
Bayesian quadrature for H^1(μ) with Poincaré inequality on a compact interval
Motivated by uncertainty quantification of complex systems, we aim at fi...
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05/17/2022
High-dimensional additive Gaussian processes under monotonicity constraints
We introduce an additive Gaussian process framework accounting for monot...
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11/23/2021
Stochastic Processes Under Linear Differential Constraints : Application to Gaussian Process Regression for the 3 Dimensional Free Space Wave Equation
Let P be a linear differential operator over 𝒟⊂ℝ^d and U = (U_x)_x ∈𝒟 a ...
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10/30/2021
A comparison of mixed-variables Bayesian optimization approaches
Most real optimization problems are defined over a mixed search space wh...
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07/01/2021
Global sensitivity analysis using derivative-based sparse Poincaré chaos expansions
Variance-based global sensitivity analysis, in particular Sobol' analysi...
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09/09/2020
Sequential construction and dimension reduction of Gaussian processes under inequality constraints
Accounting for inequality constraints, such as boundedness, monotonicity...
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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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02/07/2018
Group kernels for Gaussian process metamodels with categorical inputs
Gaussian processes (GP) are widely used as a metamodel for emulating tim...
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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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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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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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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
