Keywords:
- Bayesian optimization
- Convergence
- Covariance kernels
- Expected Improvement
- Expensive black-box optimization
- Gaussian Mixture Model
- Gaussian mixture regression
- Gaussian process
- Gaussian processes
- learning from demonstration
- Learning from demonstrations
- Linear operators
- low effective dimensionality
- REMBO
- RKHS
- Sequential Uncertainty Reduction
- Structural priors
- zonotope
Publications of David Ginsbourger sorted by title
A
| A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables, , and , in: Atmospheric Research, 203:261-274, 2018 |
[DOI] |
| A supermartingale approach to Gaussian process based sequential design of experiments, , and , in: Bernoulli, 25(4A):2883-2919, 2019 |
| Adaptive Design of Experiments for Conservative Estimation of Excursion Sets, , , , and , in: Technometrics, 2019 |
[DOI] [URL] |
C
| Conditions for the finiteness of the moments of the volume of level sets, , , and , in: Electronic Communications in Probability, 24(17), 2019 |
[DOI] [URL] |
| Contaminant source localization via Bayesian global optimization, , , and , in: Hydrology and Earth System Sciences, 23:351-369, 2019 |
[DOI] [URL] |
D
| Design of Computer Experiments Using Competing Distances Between Set-Valued Inputs, , , and , in: mODa 11 - Advances in Model-Oriented Design and Analysis, pages 123-131, Springer International Publishing, 2016 |
[DOI] |
| Differentiating the Multipoint Expected Improvement for Optimal Batch Design, , and , in: Machine Learning, Optimization, and Big Data, pages 37-48, Springer International Publishing, 2015 |
[DOI] |
E
| Estimating orthant probabilities of high dimensional Gaussian vectors with an application to set estimation, and , in: Journal of Computational and Graphical Statistics, 27(2):255-267, 2018 |
[DOI] [URL] |
G
| Global Optimization with Sparse and Local Gaussian Process Models, and , in: Machine Learning, Optimization, and Big Data, pages 185-196, Springer International Publishing, 2015 |
[DOI] |
L
| Learning from demonstration with model-based Gaussian process, , and , in: Conference on Robot Learning, 2019 |
|
N
| Non-parametric warping via local scale estimation for non-stationary Gaussian process modelling, , , and , in: Wavelets and Sparsity XVII, pages 1039421, International Society for Optics and Photonics, 2017 |
[DOI] [URL] |
O
| On ANOVA Decompositions of Kernels and Gaussian Random Field Paths, , , , and , in: Monte Carlo and Quasi-Monte Carlo Methods, pages 315-330, Springer International Publishing, 2016 |
[DOI] |
| On degeneracy and invariances of random fields paths with applications in Gaussian process modelling, , and , in: Journal of Statistical Planning and Inference, 170:117-128, 2016 |
[DOI] |
| On the choice of the low-dimensional domain for global optimization via random embeddings, , and , in: Journal of Global Optimization, 2019 |
[DOI] [URL] |
| On uncertainty quantification in hydrogeology and hydrogeophysics, , , , and , in: Advances in Water Resources, 110:166–181, 2017 |
[DOI] [URL] |
P
| Planification adaptative d'expériences numériques par paquets en contexte non stationnaire pour une étude de fissuration mécanique, , , , and , in: 23eme Congres Francais de Mecanique, 28 aout - 1er septembre 2017, Lille, France (FR), AFM, 2017 |
[URL] |
| Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding, , , and , in: Technometrics, 61(4):474-493, 2019 |
[DOI] [URL] |
Q
| Quantifying uncertainties on excursion sets under a Gaussian random field prior, , , and , in: SIAM/ASA J. Uncertainty Quantification, 4(1):850-874, 2016 |
[DOI] [URL] |
S
| Sequential Design of Computer Experiments, , in: Wiley StatsRef: Statistics Reference Online, Wiley, 2018 |
W
| Warped Gaussian processes and derivative-based sequential design for functions with heterogeneous variations, , , and , in: SIAM/ASA Journal on Uncertainty Quantification, 6(3):991-1018, 2018 |