D. Ginsbourger

发表

Dealing with asynchronicity in parallel Gaussian Process based global optimization

D. Ginsbourger, J. Janusevskis, R. L. Riche, 2010 .

Kriging is well-suited to parallelize optimization

D. Ginsbourger, R. L. Riche, L. Carraro, 2010 .

A Multi-points Criterion for Deterministic Parallel Global Optimization based on Gaussian Processes

D. Ginsbourger, R. L. Riche, L. Carraro, 2008 .

Towards GP-based optimization with finite time horizon

D. Ginsbourger, R. L. Riche, R. Riche, 2009 .

A Note on the Choice and the Estimation of Kriging Models for the Analysis of Computer Experiments

David Ginsbourger, Olivier Roustant, Anca Badea, 2007 .

Towards Gaussian Process-based Optimization with Finite Time Horizon

D. Ginsbourger, R. L. Riche, R. Riche, 2010 .

Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding

D. Ginsbourger, V. Spokoiny, Jean-Michel Loubes, 2018, Electronic Journal of Statistics.

Argumentwise invariant kernels for the approximation of invariant functions

D. Ginsbourger, L. Carraro, O. Roustant, 2012 .

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

D. Ginsbourger, O. Roustant, N. Durrande, 2013, 1308.1359.

A benchmark of kriging-based infill criteria for noisy optimization

D. Ginsbourger, V. Picheny, Tobias Wagner, 2013, Structural and Multidisciplinary Optimization.

Additive Covariance Kernels for High-Dimensional Gaussian Process Modeling

D. Ginsbourger, L. Carraro, O. Roustant, 2011, 1111.6233.

A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables

D. Ginsbourger, O. Martius, E. Madonna, 2018 .

Contaminant source localization via Bayesian global optimization

D. Ginsbourger, P. Renard, Tipaluck Krityakierne, 2017, Hydrology and Earth System Sciences.

A supermartingale approach to Gaussian process based sequential design of experiments

J. Bect, D. Ginsbourger, F. Bachoc, 2016, Bernoulli.

Invariances of random elds paths, with applications in

D. Ginsbourger, O. Roustant, N. Durrande, 2013 .

Modeling Nonstationary Extreme Dependence With Stationary Max-Stable Processes and Multidimensional Scaling

D. Ginsbourger, O. Martius, C. Chevalier, 2017, Journal of Computational And Graphical Statistics.

Additive Kernels for High-dimensional Gaussian Process Modeling

David Ginsbourger, Olivier Roustant, Nicolas Durrande, 2010 .

Gears Design with Shape Uncertainties using Monte Carlo Simulations and Kriging

D. Ginsbourger, R. L. Riche, V. Picheny, 2009 .

8 Stochastic versus DeterministicApproaches

D. Ginsbourger, P. Renard, A. Alcolea, 2016 .

On uncertainty quantification in hydrogeology and hydrogeophysics

James Irving, Fabio Nobile, Niklas Linde, 2017 .

Gaussian random field models for function approximation under structural priors and adaptive design of experiments

D. Ginsbourger, 2014 .

Reproducing kernels for spaces of zero mean functions. Application to sensitivity analysis

D. Ginsbourger, L. Carraro, O. Roustant, 2011 .

Appendix : screening effect in the time dimension in the case of Monte-Carlo convergence

D. Ginsbourger, V. Picheny, 2016 .

Stochastic versus Deterministic Approaches

Philippe Renard, David Ginsbourger, Andres Alcolea, 2013 .

Separable Monte Carlo applied to laminated composite plates reliability

David Ginsbourger, Raphael T. Haftka, Benjamin P. Smarslok, 2008 .

Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures

D. Ginsbourger, L. Dümbgen, J. Ziegel, 2022, arXiv.org.

Differentiating the Multipoint Expected Improvement for Optimal Batch Design

David Ginsbourger, Sébastien Marmin, Clément Chevalier, 2015, MOD.

On the choice of the low-dimensional domain for global optimization via random embeddings

David Ginsbourger, Olivier Roustant, Mickael Binois, 2017, Journal of Global Optimization.

Dealing with asynchronicity in kriging-based parallel global optimization

David Ginsbourger, Rodolphe Le Riche, Clément Chevalier, 2011 .

Efficient batch-sequential Bayesian optimization with moments of truncated Gaussian vectors

David Ginsbourger, D. Ginsbourger, C. Chevalier, 2016, 1609.02700.

Some experiences about optimization with Gaussian processes

Victor Picheny, David Ginsbourger, Olivier Roustant, 2012 .

A new look at Kriging for the Approximation of Noisy Simulators with Tunable Fidelity

Victor Picheny, David Ginsbourger, Yann Richet, 2008 .

Mixing Krigings for Global Optimization

David Ginsbourger, Céline Helbert, L. Carraro, 2007 .

Iterative Designs of experiments for constraint approximation

Victor Picheny, David Ginsbourger, Olivier Roustant, 2008 .

Disintegration of Gaussian Measures for Sequential Assimilation of Linear Operator Data

D. Ginsbourger, C'edric Travelletti, Cédric Travelletti, 2022, 2207.13581.

Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariances

D. Ginsbourger, Cedric Scharer, 2021, 2101.03108.

Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures

D. Ginsbourger, L. Dümbgen, Johanna Fasciati-Ziegel, 2022, Bernoulli.

Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding

David Ginsbourger, Jean-Michel Loubes, François Bachoc, 2018, Electronic Journal of Statistics.

A Multi-points Criterion for Deterministic Parallel Global Optimization based on Kriging

David Ginsbourger, Rodolphe Le Riche, Laurent Carraro, 2007 .

Area-covering postprocessing of ensemble precipitation forecasts using topographical and seasonal conditions

David Ginsbourger, Jonas Bhend, Lea Friedli, 2019, Stochastic Environmental Research and Risk Assessment.

Quantifying and reducing uncertainties on excursion sets under a gaussian random field prior

David Ginsbourger, Julien Bect, Ilya Molchanov, 2014 .

Uncertainty Quantification and Experimental Design for large-scale linear Inverse Problems under Gaussian Process Priors

D. Ginsbourger, N. Linde, C'edric Travelletti, 2021, SIAM/ASA Journal on Uncertainty Quantification.

A benchmark of kriging-based infill criteria for noisy optimization

D. Ginsbourger, V. Picheny, Tobias Wagner, 2013, Structural and Multidisciplinary Optimization.

Adaptive data-driven selection of sequences of biological and cognitive markers in clinical diagnosis of dementia

D. Ginsbourger, C. Davatzikos, A. Abdulkadir, 2021, medRxiv.

KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging

Victor Picheny, David Ginsbourger, Clément Chevalier, 2014, Comput. Stat. Data Anal..

Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling

D. Ginsbourger, K. Rajan, J. Eidsvik, 2020, ArXiv.

Sequential Design of Computer Experiments

D. Ginsbourger, 2018, Wiley StatsRef: Statistics Reference Online.

Estimating Orthant Probabilities of High-Dimensional Gaussian Vectors with An Application to Set Estimation

David Ginsbourger, Dario Azzimonti, D. Ginsbourger, 2016, 1603.05031.

A chapter submitted for publication in the book Environmental Modelling: Finding Simplicity in Complexity edited by John Wainwright and Mark Mulligan. Willey-Blackwell Publishers

D. Ginsbourger, P. Renard, A. Alcolea, 2009 .

DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization

Yves Deville, David Ginsbourger, Olivier Roustant, 2012 .

Sequential design of computer experiments for the estimation of a probability of failure

Ling Li, Victor Picheny, David Ginsbourger, 2010, Statistics and Computing.

Global Optimization with Sparse and Local Gaussian Process Models

David Ginsbourger, Tipaluck Krityakierne, D. Ginsbourger, 2015, MOD.

Quantile-Based Optimization of Noisy Computer Experiments With Tunable Precision

Victor Picheny, David Ginsbourger, Yann Richet, 2013, Technometrics.

Adaptive Designs of Experiments for Accurate Approximation of a Target Region

Victor Picheny, David Ginsbourger, Olivier Roustant, 2010 .

Fast ABC with joint generative modelling and subset simulation

D. Ginsbourger, N. Linde, Eliane Maalouf, 2021, LOD.

A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations

Victor Picheny, David Ginsbourger, D. Ginsbourger, 2013, SIAM/ASA J. Uncertain. Quantification.

Additive Kernels for Gaussian Process Modeling

David Ginsbourger, Olivier Roustant, Nicolas Durrande, 2011, 1103.4023.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

David Ginsbourger, Olivier Roustant, Mickaël Binois, 2015, Eur. J. Oper. Res..

Bayesian Adaptive Reconstruction of Profile Optima and Optimizers

Jean Baccou, David Ginsbourger, Nicolas Garland, 2014, SIAM/ASA J. Uncertain. Quantification.

Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package

Victor Picheny, David Ginsbourger, D. Ginsbourger, 2014, Comput. Stat. Data Anal..

Corrected Kriging Update Formulae for Batch-Sequential Data Assimilation

David Ginsbourger, D. Ginsbourger, C. Chevalier, 2012, 1203.6452.

Using the Efficient Global Optimization Algorithm to Assist Nuclear Criticality Safety Assessment

Victor Picheny, David Ginsbourger, Yann Richet, 2013 .

Distance-based Kriging relying on proxy simulations for inverse conditioning

Philippe Renard, David Ginsbourger, Guillaume Pirot, 2013 .

Fast Computation of the Multi-Points Expected Improvement with Applications in Batch Selection

David Ginsbourger, Clément Chevalier, D. Ginsbourger, 2013, LION.

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

David Ginsbourger, Olivier Roustant, Laurent Carraro, 2011, J. Multivar. Anal..

Kernels and Designs for Modelling Invariant Functions: From Group Invariance to Additivity

David Ginsbourger, Olivier Roustant, Nicolas Durrande, 2013 .

Estimating and quantifying uncertainties on level sets using the Vorob'ev expectation and deviation with Gaussian process models

David Ginsbourger, Julien Bect, Ilya Molchanov, 2013 .

Changes in the odds of extreme events in the Atlantic basin depending on the position of the extratropical jet

D. Ginsbourger, O. Martius, C. Chevalier, 2012 .

Parallel Budgeted Optimization Applied to the Design of an Air Duct

David Ginsbourger, Rodolphe Le Riche, Ramunas Girdziusas, 2012 .

Expected Improvements for the Asynchronous Parallel Global Optimization of Expensive Functions: Potentials and Challenges

David Ginsbourger, Rodolphe Le Riche, Ramunas Girdziusas, 2012, LION.

Noisy Expected Improvement and on-line computation time allocation for the optimization of simulators with tunable fidelity

David Ginsbourger, Yann Richet, Victor Picheny, 2010 .

Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization

D. Ginsbourger, Tipaluck Krityakierne, Poompol Buathong, 2019, AISTATS.

Adaptive Design of Experiments for Conservative Estimation of Excursion Sets

David Ginsbourger, Yann Richet, Julien Bect, 2016, Technometrics.

Fast Update of Conditional Simulation Ensembles

David Ginsbourger, Xavier Emery, Clément Chevalier, 2015, Mathematical Geosciences.

Fast Parallel Kriging-Based Stepwise Uncertainty Reduction With Application to the Identification of an Excursion Set

Victor Picheny, David Ginsbourger, Yann Richet, 2014, Technometrics.

Quantifying Uncertainties on Excursion Sets Under a Gaussian Random Field Prior

David Ginsbourger, Julien Bect, Dario Azzimonti, 2015, SIAM/ASA J. Uncertain. Quantification.

Discrete mixtures of kernels for Kriging‐based optimization

David Ginsbourger, Céline Helbert, Laurent Carraro, 2008, Qual. Reliab. Eng. Int..

Fast kriging-based stepwise uncertainty reduction with application to the identification of an excursion set

Victor Picheny, David Ginsbourger, Julien Bect, 2019 .

A Warped Kernel Improving Robustness in Bayesian Optimization Via Random Embeddings

David Ginsbourger, Olivier Roustant, Mickaël Binois, 2014, LION.

Warped Gaussian Processes and Derivative-Based Sequential Designs for Functions with Heterogeneous Variations

Jacques Liandrat, Jean Baccou, David Ginsbourger, 2018, SIAM/ASA J. Uncertain. Quantification.

Approximating computer experiments using partially converged simulations and a Gaussian process emulator

Victor Picheny, David Ginsbourger, D. Ginsbourger, 2012 .

Inference of geostatistical hyperparameters with the correlated pseudo-marginal method

A. Doucet, D. Ginsbourger, N. Linde, 2023, Advances in Water Resources.

A Note on the Choice and the Estimation of Kriging Models for the Analysis of Computer Experiments

David Ginsbourger, Olivier Roustant, Anca Badea, 2007 .

Lithological tomography with the correlated pseudo-marginal method

Arnaud Doucet, David Ginsbourger, Niklas Linde, 2021, Geophysical Journal International.

Improving accuracy of failure probability estimates with separable Monte Carlo

David Ginsbourger, Raphael T. Haftka, Benjamin P. Smarslok, 2010 .

Separable Monte Carlo Simulation Applied to Laminated Composite Plates Reliability

David Ginsbourger, Raphael T. Haftka, Benjamin P. Smarslok, 2008 .

Learning from demonstration with model-based Gaussian process

David Ginsbourger, Sylvain Calinon, No'emie Jaquier, 2019, CoRL.

Profile Extrema for Visualizing and Quantifying Uncertainties on Excursion Regions: Application to Coastal Flooding

David Ginsbourger, Dario Azzimonti, J'er'emy Rohmer, 2017, Technometrics.

On degeneracy and invariances of random fields paths with applications in Gaussian process modelling

David Ginsbourger, Olivier Roustant, Nicolas Durrande, 2016 .

On ANOVA Decompositions of Kernels and Gaussian Random Field Paths

David Ginsbourger, Olivier Roustant, Dominic Schuhmacher, 2014, MCQMC.

High-Dimensional Model-Based Optimization Based on Noisy Evaluations of Computer Games

Mike Preuss, David Ginsbourger, Tobias Wagner, 2012, LION.

Evaluating Forecasts for High-Impact Events Using Transformed Kernel Scores

D. Ginsbourger, J. Ziegel, S. Allen, 2022, SIAM/ASA Journal on Uncertainty Quantification.

Assessing the calibration of multivariate probabilistic forecasts

D. Ginsbourger, J. Ziegel, S. Allen, 2023, 2307.05846.

Continuous logistic Gaussian random measure fields for spatial distributional modelling

D. Ginsbourger, Ath'enais Gautier, 2021, 2110.02876.

Goal-oriented adaptive sampling under random field modelling of response probability distributions

D. Ginsbourger, G. Pirot, Ath'enais Gautier, 2021, ESAIM: Proceedings and Surveys.

An energy-based model approach to rare event probability estimation

D. Ginsbourger, Lea Friedli, Arnaud Doucet, 2023, 2310.04082.

Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding

D. Ginsbourger, V. Spokoiny, Jean-Michel Loubes, 2018, Electronic Journal of Statistics.

Conditions for the finiteness of the moments of the volume of level sets

D. Ginsbourger, Jean-marc Azais, J. León, 2018, Electronic Communications in Probability.

Functional error modeling for uncertainty quantification in hydrogeology

David Ginsbourger, Ivan Lunati, Laureline Josset, 2015 .

Consistency of some sequential experimental design strategies for excursion set estimation based on vector-valued Gaussian processes

D. Ginsbourger, Philip Stange, 2023, 2310.07315.

Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia

D. Ginsbourger, C. Davatzikos, A. Abdulkadir, 2023, Scientific reports.

Non-parametric warping via local scale estimation for non-stationary Gaussian process modelling

Jacques Liandrat, Jean Baccou, David Ginsbourger, 2017, Optical Engineering + Applications.

Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization

D. Ginsbourger, Tipaluck Krityakierne, Poompol Buathong, 2019, AISTATS.