David Ginsbourger

Cédric Schärer

J. Comput. Graph. Stat., 2025

2024

Efficient pooling of predictions via kernel embeddings.

[BibT_eX]

[DOI]

Sam Allen

Johanna Ziegel

CoRR, 2024

2023

Evaluating Forecasts for High-Impact Events Using Transformed Kernel Scores.

[BibT_eX]

[DOI]

Sam Allen

Johanna Ziegel

SIAM/ASA J. Uncertain. Quantification, September, 2023

Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors.

[BibT_eX]

[DOI]

Cédric Travelletti

Niklas Linde

SIAM/ASA J. Uncertain. Quantification, March, 2023

Non-Sequential Ensemble Kalman Filtering using Distributed Arrays.

[BibT_eX]

[DOI]

CoRR, 2023

2022

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

[BibT_eX]

[DOI]

Johanna Ziegel

Lutz Dümbgen

CoRR, 2022

2021

Adaptive Design of Experiments for Conservative Estimation of Excursion Sets.

[BibT_eX]

[DOI]

Technometrics, 2021

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

[BibT_eX]

[DOI]

Olivia Martius

J. Comput. Graph. Stat., 2021

Fast ABC with Joint Generative Modelling and Subset Simulation.

[BibT_eX]

[DOI]

Eliane Maalouf

Niklas Linde

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.

[BibT_eX]

[DOI]

Mickaël Binois

Olivier Roustant

J. Glob. Optim., 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

Poompol Buathong

Tipaluck Krityakierne

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.

[BibT_eX]

[DOI]

Technometrics, 2019

Learning from demonstration with model-based Gaussian process.

[BibT_eX]

[DOI]

Noémie Jaquier

Sylvain Calinon

Proceedings of the 3rd Annual Conference on Robot Learning, 2019

2018

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

[BibT_eX]

[DOI]

SIAM/ASA J. Uncertain. Quantification, 2018

2016

Comment: Some Enhancements Over the Augmented Lagrangian Approach.

[BibT_eX]

[DOI]

Tipaluck Krityakierne

Technometrics, 2016

Quantifying Uncertainties on Excursion Sets Under a Gaussian Random Field Prior.

[BibT_eX]

[DOI]

SIAM/ASA J. Uncertain. Quantification, 2016

2015

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations.

[BibT_eX]

[DOI]

Mickaël Binois

Olivier Roustant

Eur. J. Oper. Res., 2015

Differentiating the Multipoint Expected Improvement for Optimal Batch Design.

[BibT_eX]

[DOI]

Sébastien Marmin

Proceedings of the Machine Learning, Optimization, and Big Data, 2015

Global Optimization with Sparse and Local Gaussian Process Models.

[BibT_eX]

[DOI]

Tipaluck Krityakierne

Proceedings of the Machine Learning, Optimization, and Big Data, 2015

A Warped Kernel Improving Robustness in Bayesian Optimization Via Random Embeddings.

[BibT_eX]

[DOI]

Mickaël Binois

Olivier Roustant

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.

[BibT_eX]

[DOI]

Technometrics, 2014

Bayesian Adaptive Reconstruction of Profile Optima and Optimizers.

[BibT_eX]

[DOI]

SIAM/ASA J. Uncertain. Quantification, 2014

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

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2014

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

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2014

On ANOVA Decompositions of Kernels and Gaussian Random Field Paths.

[BibT_eX]

[DOI]

Proceedings of the Monte Carlo and Quasi-Monte Carlo Methods, 2014

2013

Rejoinder.

[BibT_eX]

[DOI]

Technometrics, 2013

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

[BibT_eX]

[DOI]

Technometrics, 2013

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

[BibT_eX]

[DOI]

J. Multivar. Anal., 2013

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

[BibT_eX]

[DOI]

SIAM/ASA J. Uncertain. Quantification, 2013

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Stat. Comput., 2012

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

[BibT_eX]

[DOI]

Mike Preuss

Tobias Wagner

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.

[BibT_eX]

[DOI]

Proceedings of the Learning and Intelligent Optimization - 6th International Conference, 2012

2008

Discrete mixtures of kernels for Kriging-based optimization.

[BibT_eX]

[DOI]