David Ginsbourger

Orcid: 0000-0003-2724-2678

According to our database1, David Ginsbourger authored at least 33 papers between 2008 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Evaluating Forecasts for High-Impact Events Using Transformed Kernel Scores.
SIAM/ASA J. Uncertain. Quantification, September, 2023

Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors.
SIAM/ASA J. Uncertain. Quantification, March, 2023

Non-Sequential Ensemble Kalman Filtering using Distributed Arrays.
CoRR, 2023

2022
Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures.
CoRR, 2022

2021
Adaptive Design of Experiments for Conservative Estimation of Excursion Sets.
Technometrics, 2021

Modeling Nonstationary Extreme Dependence With Stationary Max-Stable Processes and Multidimensional Scaling.
J. Comput. Graph. Stat., 2021

Fast ABC with Joint Generative Modelling and Subset Simulation.
Proceedings of the Machine Learning, Optimization, and Data Science, 2021

2020
On the choice of the low-dimensional domain for global optimization via random embeddings.
J. Glob. Optim., 2020

Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling.
CoRR, 2020

Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Profile Extrema for Visualizing and Quantifying Uncertainties on Excursion Regions: Application to Coastal Flooding.
Technometrics, 2019

Learning from demonstration with model-based Gaussian process.
Proceedings of the 3rd Annual Conference on Robot Learning, 2019

2018
Warped Gaussian Processes and Derivative-Based Sequential Designs for Functions with Heterogeneous Variations.
SIAM/ASA J. Uncertain. Quantification, 2018

2016
Comment: Some Enhancements Over the Augmented Lagrangian Approach.
Technometrics, 2016

Quantifying Uncertainties on Excursion Sets Under a Gaussian Random Field Prior.
SIAM/ASA J. Uncertain. Quantification, 2016

2015
Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations.
Eur. J. Oper. Res., 2015

Differentiating the Multipoint Expected Improvement for Optimal Batch Design.
Proceedings of the Machine Learning, Optimization, and Big Data, 2015

Global Optimization with Sparse and Local Gaussian Process Models.
Proceedings of the Machine Learning, Optimization, and Big Data, 2015

A Warped Kernel Improving Robustness in Bayesian Optimization Via Random Embeddings.
Proceedings of the Learning and Intelligent Optimization - 9th International Conference, 2015

2014
Fast Parallel Kriging-Based Stepwise Uncertainty Reduction With Application to the Identification of an Excursion Set.
Technometrics, 2014

Bayesian Adaptive Reconstruction of Profile Optima and Optimizers.
SIAM/ASA J. Uncertain. Quantification, 2014

Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package.
Comput. Stat. Data Anal., 2014

KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging.
Comput. Stat. Data Anal., 2014

On ANOVA Decompositions of Kernels and Gaussian Random Field Paths.
Proceedings of the Monte Carlo and Quasi-Monte Carlo Methods, 2014

2013
Rejoinder.
Technometrics, 2013

Quantile-Based Optimization of Noisy Computer Experiments With Tunable Precision.
Technometrics, 2013

ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis.
J. Multivar. Anal., 2013

A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations.
SIAM/ASA J. Uncertain. Quantification, 2013

Fast Computation of the Multi-Points Expected Improvement with Applications in Batch Selection.
Proceedings of the Learning and Intelligent Optimization - 7th International Conference, 2013

2012
Sequential design of computer experiments for the estimation of a probability of failure.
Stat. Comput., 2012

High-Dimensional Model-Based Optimization Based on Noisy Evaluations of Computer Games.
Proceedings of the Learning and Intelligent Optimization - 6th International Conference, 2012

Expected Improvements for the Asynchronous Parallel Global Optimization of Expensive Functions: Potentials and Challenges.
Proceedings of the Learning and Intelligent Optimization - 6th International Conference, 2012

2008
Discrete mixtures of kernels for Kriging-based optimization.
Qual. Reliab. Eng. Int., 2008


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