David Ginsbourger

43 papers A* 1A 1Misc 1Journal 32Unranked 8

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	An Energy-Based Model Approach to Rare Event Probability Estimation. SIAM/ASA J. Uncertain. Quantification Lea Friedli, David Ginsbourger, Arnaud Doucet, Niklas Linde
2025	J	`jnl`	CRPS-Based Targeted Sequential Design with Application in Chemical Space. CoRR Lea Friedli, Athénaïs Gautier, Anna Broccard, David Ginsbourger
2025	J	`jnl`	Efficient pooling of predictions via kernel embeddings. Trans. Mach. Learn. Res. Sam Allen, David Ginsbourger, Johanna Ziegel
2025	J	`jnl`	Fast Calculation of Gaussian Process Multiple-Fold Cross-Validation Residuals and their Covariances. J. Comput. Graph. Stat. David Ginsbourger, Cédric Schärer
2025	A*	`conf`	Integration-free Kernels for Equivariant Gaussian Process Modelling. ICML Tim Steinert, David Ginsbourger, August Lykke-Møller, Ove Christiansen, Henry Moss
2024	J	`jnl`	Efficient pooling of predictions via kernel embeddings. CoRR Sam Allen, David Ginsbourger, Johanna Ziegel
2023	J	`jnl`	Evaluating Forecasts for High-Impact Events Using Transformed Kernel Scores. SIAM/ASA J. Uncertain. Quantification Sam Allen, David Ginsbourger, Johanna Ziegel
2023	J	`jnl`	Non-Sequential Ensemble Kalman Filtering using Distributed Arrays. CoRR Cédric Travelletti, Jörg Franke, David Ginsbourger, Stefan Brönnimann
2023	J	`jnl`	Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors. SIAM/ASA J. Uncertain. Quantification Cédric Travelletti, David Ginsbourger, Niklas Linde
2022	J	`jnl`	Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures. CoRR Johanna Ziegel, David Ginsbourger, Lutz Dümbgen
2021	J	`jnl`	Adaptive Design of Experiments for Conservative Estimation of Excursion Sets. Technometrics Dario Azzimonti, David Ginsbourger, Clément Chevalier, Julien Bect, Yann Richet
2021	—	`conf`	Fast ABC with Joint Generative Modelling and Subset Simulation. LOD Eliane Maalouf, David Ginsbourger, Niklas Linde
2021	J	`jnl`	Fast ABC with joint generative modelling and subset simulation. CoRR Eliane Maalouf, David Ginsbourger, Niklas Linde
2021	J	`jnl`	Modeling Nonstationary Extreme Dependence With Stationary Max-Stable Processes and Multidimensional Scaling. J. Comput. Graph. Stat. Clément Chevalier, Olivia Martius, David Ginsbourger
2021	J	`jnl`	Uncertainty Quantification and Experimental Design for large-scale linear Inverse Problems under Gaussian Process Priors. CoRR Cédric Travelletti, David Ginsbourger, Niklas Linde
2020	A	`conf`	Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization. AISTATS Poompol Buathong, David Ginsbourger, Tipaluck Krityakierne
2020	J	`jnl`	Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling. CoRR Trygve Olav Fossum, Cédric Travelletti, Jo Eidsvik, David Ginsbourger, Kanna Rajan
2020	J	`jnl`	On the choice of the low-dimensional domain for global optimization via random embeddings. J. Glob. Optim. Mickaël Binois, David Ginsbourger, Olivier Roustant
2019	J	`jnl`	Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization. CoRR Poompol Buathong, David Ginsbourger, Tipaluck Krityakierne
2019	Misc	`conf`	Learning from demonstration with model-based Gaussian process. CoRL Noémie Jaquier, David Ginsbourger, Sylvain Calinon
2019	J	`jnl`	Learning from demonstration with model-based Gaussian process. CoRR Noémie Jaquier, David Ginsbourger, Sylvain Calinon
2019	J	`jnl`	Profile Extrema for Visualizing and Quantifying Uncertainties on Excursion Regions: Application to Coastal Flooding. Technometrics Dario Azzimonti, David Ginsbourger, Jérémy Rohmer, Déborah Idier
2018	J	`jnl`	Warped Gaussian Processes and Derivative-Based Sequential Designs for Functions with Heterogeneous Variations. SIAM/ASA J. Uncertain. Quantification Sébastien Marmin, David Ginsbourger, Jean Baccou, Jacques Liandrat
2016	J	`jnl`	Comment: Some Enhancements Over the Augmented Lagrangian Approach. Technometrics Victor Picheny, David Ginsbourger, Tipaluck Krityakierne
2016	J	`jnl`	Quantifying Uncertainties on Excursion Sets Under a Gaussian Random Field Prior. SIAM/ASA J. Uncertain. Quantification Dario Azzimonti, Julien Bect, Clément Chevalier, David Ginsbourger
2015	—	`conf`	A Warped Kernel Improving Robustness in Bayesian Optimization Via Random Embeddings. LION Mickaël Binois, David Ginsbourger, Olivier Roustant
2015	—	`conf`	Differentiating the Multipoint Expected Improvement for Optimal Batch Design. MOD Sébastien Marmin, Clément Chevalier, David Ginsbourger
2015	—	`conf`	Global Optimization with Sparse and Local Gaussian Process Models. MOD Tipaluck Krityakierne, David Ginsbourger
2015	J	`jnl`	Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations. Eur. J. Oper. Res. Mickaël Binois, David Ginsbourger, Olivier Roustant
2014	J	`jnl`	Bayesian Adaptive Reconstruction of Profile Optima and Optimizers. SIAM/ASA J. Uncertain. Quantification David Ginsbourger, Jean Baccou, Clément Chevalier, Frédéric Perales, Nicolas Garland, Yann Monerie
2014	J	`jnl`	Fast Parallel Kriging-Based Stepwise Uncertainty Reduction With Application to the Identification of an Excursion Set. Technometrics Clément Chevalier, Julien Bect, David Ginsbourger, Emmanuel Vázquez, Victor Picheny, Yann Richet
2014	J	`jnl`	KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging. Comput. Stat. Data Anal. Clément Chevalier, Victor Picheny, David Ginsbourger
2014	J	`jnl`	Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package. Comput. Stat. Data Anal. Victor Picheny, David Ginsbourger
2014	—	`conf`	On ANOVA Decompositions of Kernels and Gaussian Random Field Paths. MCQMC David Ginsbourger, Olivier Roustant, Dominic Schuhmacher, Nicolas Durrande, Nicolas Lenz
2013	J	`jnl`	A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations. SIAM/ASA J. Uncertain. Quantification Victor Picheny, David Ginsbourger
2013	J	`jnl`	ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis. J. Multivar. Anal. Nicolas Durrande, David Ginsbourger, Olivier Roustant, Laurent Carraro
2013	—	`conf`	Fast Computation of the Multi-Points Expected Improvement with Applications in Batch Selection. LION Clément Chevalier, David Ginsbourger
2013	J	`jnl`	Quantile-Based Optimization of Noisy Computer Experiments With Tunable Precision. Technometrics Victor Picheny, David Ginsbourger, Yann Richet, Gregory Caplin
2013	J	`jnl`	Rejoinder. Technometrics Victor Picheny, David Ginsbourger, Yann Richet, Gregory Caplin
2012	—	`conf`	Expected Improvements for the Asynchronous Parallel Global Optimization of Expensive Functions: Potentials and Challenges. LION Janis Janusevskis, Rodolphe Le Riche, David Ginsbourger, Ramunas Girdziusas
2012	—	`conf`	High-Dimensional Model-Based Optimization Based on Noisy Evaluations of Computer Games. LION Mike Preuss, Tobias Wagner, David Ginsbourger
2012	J	`jnl`	Sequential design of computer experiments for the estimation of a probability of failure. Stat. Comput. Julien Bect, David Ginsbourger, Ling Li, Victor Picheny, Emmanuel Vázquez
2008	J	`jnl`	Discrete mixtures of kernels for Kriging-based optimization. Qual. Reliab. Eng. Int. David Ginsbourger, Céline Helbert, Laurent Carraro