Discrete mixtures of kernels for Kriging-based optimization. - David Ginsbourger; Céline Helbert; Laurent Carraro (Qual. Reliab. Eng. Int., 2008)

Expected Improvements for the Asynchronous Parallel Global Optimization of Expensive Functions: Potentials and Challenges. - Janis Janusevskis; Rodolphe Le Riche; David Ginsbourger; Ramunas Girdziusas (LION, 2012)

High-Dimensional Model-Based Optimization Based on Noisy Evaluations of Computer Games. - Mike Preuss; Tobias Wagner 001; David Ginsbourger (LION, 2012)

Sequential design of computer experiments for the estimation of a probability of failure. - Julien Bect; David Ginsbourger; Ling Li; Victor Picheny; Emmanuel Vázquez (Stat. Comput., 2012)

Fast Computation of the Multi-Points Expected Improvement with Applications in Batch Selection. - Clément Chevalier; David Ginsbourger (LION, 2013)

A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations. - Victor Picheny; David Ginsbourger (SIAM/ASA J. Uncertain. Quantification, 2013)

ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis. - Nicolas Durrande; David Ginsbourger; Olivier Roustant; Laurent Carraro (J. Multivar. Anal., 2013)

Quantile-Based Optimization of Noisy Computer Experiments With Tunable Precision. - Victor Picheny; David Ginsbourger; Yann Richet; Gregory Caplin (Technometrics, 2013)

Rejoinder. - Victor Picheny; David Ginsbourger; Yann Richet; Gregory Caplin (Technometrics, 2013)

On ANOVA Decompositions of Kernels and Gaussian Random Field Paths. - David Ginsbourger; Olivier Roustant; Dominic Schuhmacher; Nicolas Durrande; Nicolas Lenz (MCQMC, 2014)

KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging. - Clément Chevalier; Victor Picheny; David Ginsbourger (Comput. Stat. Data Anal., 2014)

Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package. - Victor Picheny; David Ginsbourger (Comput. Stat. Data Anal., 2014)

Bayesian Adaptive Reconstruction of Profile Optima and Optimizers. - David Ginsbourger; Jean Baccou; Clément Chevalier; Frédéric Perales; Nicolas Garland; Yann Monerie (SIAM/ASA J. Uncertain. Quantification, 2014)

Fast Parallel Kriging-Based Stepwise Uncertainty Reduction With Application to the Identification of an Excursion Set. - Clément Chevalier; Julien Bect; David Ginsbourger; Emmanuel Vázquez; Victor Picheny; Yann Richet (Technometrics, 2014)

A Warped Kernel Improving Robustness in Bayesian Optimization Via Random Embeddings. - Mickaël Binois; David Ginsbourger; Olivier Roustant (LION, 2015)

Global Optimization with Sparse and Local Gaussian Process Models. - Tipaluck Krityakierne; David Ginsbourger (MOD, 2015)

Differentiating the Multipoint Expected Improvement for Optimal Batch Design. - Sébastien Marmin; Clément Chevalier; David Ginsbourger (MOD, 2015)

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations. - Mickaël Binois; David Ginsbourger; Olivier Roustant (Eur. J. Oper. Res., 2015)

Quantifying Uncertainties on Excursion Sets Under a Gaussian Random Field Prior. - Dario Azzimonti; Julien Bect; Clément Chevalier; David Ginsbourger (SIAM/ASA J. Uncertain. Quantification, 2016)

Comment: Some Enhancements Over the Augmented Lagrangian Approach. - Victor Picheny; David Ginsbourger; Tipaluck Krityakierne (Technometrics, 2016)

Warped Gaussian Processes and Derivative-Based Sequential Designs for Functions with Heterogeneous Variations. - Sébastien Marmin; David Ginsbourger; Jean Baccou; Jacques Liandrat (SIAM/ASA J. Uncertain. Quantification, 2018)

Learning from demonstration with model-based Gaussian process. - Noémie Jaquier; David Ginsbourger; Sylvain Calinon (CoRL, 2019)

Profile Extrema for Visualizing and Quantifying Uncertainties on Excursion Regions: Application to Coastal Flooding. - Dario Azzimonti; David Ginsbourger; Jérémy Rohmer 001; Déborah Idier (Technometrics, 2019)

Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization. - Poompol Buathong; David Ginsbourger; Tipaluck Krityakierne (AISTATS, 2020)

Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling. - Trygve Olav Fossum; Cédric Travelletti; Jo Eidsvik; David Ginsbourger; Kanna Rajan (CoRR, 2020)

On the choice of the low-dimensional domain for global optimization via random embeddings. - Mickaël Binois; David Ginsbourger; Olivier Roustant (J. Glob. Optim., 2020)

Fast ABC with Joint Generative Modelling and Subset Simulation. - Eliane Maalouf; David Ginsbourger; Niklas Linde (LOD, 2021)

Modeling Nonstationary Extreme Dependence With Stationary Max-Stable Processes and Multidimensional Scaling. - Clément Chevalier; Olivia Martius; David Ginsbourger (J. Comput. Graph. Stat., 2021)

Adaptive Design of Experiments for Conservative Estimation of Excursion Sets. - Dario Azzimonti; David Ginsbourger; Clément Chevalier; Julien Bect; Yann Richet (Technometrics, 2021)

Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures. - Johanna Ziegel; David Ginsbourger; Lutz Dümbgen (CoRR, 2022)

Non-Sequential Ensemble Kalman Filtering using Distributed Arrays. - Cédric Travelletti; Jörg Franke; David Ginsbourger; Stefan Brönnimann (CoRR, 2023)

Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors. - Cédric Travelletti; David Ginsbourger; Niklas Linde (SIAM/ASA J. Uncertain. Quantification, 2023)

Evaluating Forecasts for High-Impact Events Using Transformed Kernel Scores. - Sam Allen; David Ginsbourger; Johanna Ziegel (SIAM/ASA J. Uncertain. Quantification, 2023)

Efficient pooling of predictions via kernel embeddings. - Sam Allen; David Ginsbourger; Johanna Ziegel (CoRR, 2024)

Fast Calculation of Gaussian Process Multiple-Fold Cross-Validation Residuals and their Covariances. - David Ginsbourger; Cédric Schärer (J. Comput. Graph. Stat., 2025)

An Energy-Based Model Approach to Rare Event Probability Estimation. - Lea Friedli; David Ginsbourger; Arnaud Doucet; Niklas Linde (SIAM/ASA J. Uncertain. Quantification, 2025)

CRPS-Based Targeted Sequential Design with Application in Chemical Space. - Lea Friedli; Athénaïs Gautier; Anna Broccard; David Ginsbourger (CoRR, 2025)