David Ginsbourger

David Ginsbourger

Universität Bern

H-index: 36

Europe-Switzerland

About David Ginsbourger

David Ginsbourger, With an exceptional h-index of 36 and a recent h-index of 26 (since 2020), a distinguished researcher at Universität Bern, specializes in the field of Gaussian Processes, Bayesian optimization, Uncertainty Quantification, Inversion, Kernels.

His recent articles reflect a diverse array of research interests and contributions to the field:

Some combinatorics of data leakage induced by clusters

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

Evaluating forecasts for high-impact events using transformed kernel scores

Inference of geostatistical hyperparameters with the correlated pseudo-marginal method

Assessing the calibration of multivariate probabilistic forecasts

Adaptive data-driven selection of sequences of biological and cognitive markers in clinical diagnosis of dementia

Non-Sequential Ensemble Kalman Filtering using Distributed Arrays

Continuous logistic Gaussian random measure fields for spatial distributional modelling

David Ginsbourger Information

University

Position

___

Citations(all)

5303

Citations(since 2020)

3084

Cited By

3638

hIndex(all)

36

hIndex(since 2020)

26

i10Index(all)

60

i10Index(since 2020)

49

Email

University Profile Page

Universität Bern

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David Ginsbourger Skills & Research Interests

Gaussian Processes

Bayesian optimization

Uncertainty Quantification

Inversion

Kernels

Top articles of David Ginsbourger

Title

Journal

Author(s)

Publication Date

Some combinatorics of data leakage induced by clusters

Stochastic Environmental Research and Risk Assessment

Fabian Guignard

David Ginsbourger

Lilia Levy Häner

Juan Manuel Herrera

2024/4/11

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

Bernoulli

Johanna Ziegel

David Ginsbourger

Lutz Dümbgen

2024

Evaluating forecasts for high-impact events using transformed kernel scores

SIAM/ASA Journal on Uncertainty Quantification

Sam Allen

David Ginsbourger

Johanna Ziegel

2023/9

Inference of geostatistical hyperparameters with the correlated pseudo-marginal method

Advances in Water Resources

Lea Friedli

Niklas Linde

David Ginsbourger

Alejandro Fernandez Visentini

Arnaud Doucet

2023

Assessing the calibration of multivariate probabilistic forecasts

Quarterly Journal of the Royal Meteorological Society

Sam Allen

Johanna Ziegel

David Ginsbourger

2023/7/11

Adaptive data-driven selection of sequences of biological and cognitive markers in clinical diagnosis of dementia

medRxiv

Patric Wyss

David Ginsbourger

Haochang Shou

Christos Davatzikos

Stefan Klöppel

...

2022/1/1

Non-Sequential Ensemble Kalman Filtering using Distributed Arrays

arXiv preprint arXiv:2311.12909

Cédric Travelletti

Jörg Franke

David Ginsbourger

Stefan Brönnimann

2023/11/21

Continuous logistic Gaussian random measure fields for spatial distributional modelling

arXiv preprint arXiv:2110.02876v2

Athénaïs Gautier

David Ginsbourger

2023/6/9

Consistency of some sequential experimental design strategies for excursion set estimation based on vector-valued Gaussian processes

arXiv preprint arXiv:2310.07315

Philip Stange

David Ginsbourger

2023/10/11

Fast calculation of gaussian process multiple-fold cross-validation residuals and their covariances

arXiv preprint arXiv:2101.03108v2

David Ginsbourger

Cedric Schärer

2023/6/3

An energy-based model approach to rare event probability estimation

arXiv preprint arXiv:2310.04082

Lea Friedli

David Ginsbourger

Arnaud Doucet

Niklas Linde

2023/10/6

Uncertainty Quantification and Experimental Design for large-scale linear Inverse Problems under Gaussian Process Priors

SIAM/ASA Journal on Uncertainty Quantification

Cédric Travelletti

David Ginsbourger

Niklas Linde

2023

Disintegration of Gaussian measures for sequential assimilation of linear operator data

arXiv preprint arXiv:2207.13581

Cédric Travelletti

David Ginsbourger

2022/7/27

Fast ABC with joint generative modelling and subset simulation

Eliane Maalouf

David Ginsbourger

Niklas Linde

2021/10/4

Lithological tomography with the correlated pseudo-marginal method

Geophysical Journal International

Lea Friedli

Niklas Linde

David Ginsbourger

Arnaud Doucet

2022

Goal-oriented adaptive sampling under random field modelling of response probability distributions

Athénaïs Gautier

David Ginsbourger

Guillaume Pirot

2021/9/1

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

The Annals of Applied Statistics

Trygve Olav Fossum

Cédric Travelletti

Jo Eidsvik

David Ginsbourger

Kanna Rajan

2021/6

Area-covering postprocessing of ensemble precipitation forecasts using topographical and seasonal conditions

Stochastic Environmental Research and Risk Assessment

Lea Friedli

David Ginsbourger

Jonas Bhend

2021

Adaptive Design of Experiments for Conservative Estimation of Excursion Sets

Technometrics

Dario Azzimonti

David Ginsbourger

Clément Chevalier

Julien Bect

Yann Richet

2021

Probabilistic ABC with Spatial Logistic Gaussian Process modelling

Third Workshop on Machine Learning and the Physical Sciences

Athénaıs Gautier

David Ginsbourger

Guillaume Pirot

2020

See List of Professors in David Ginsbourger University(Universität Bern)

Co-Authors

H-index: 92
Raphael Haftka

Raphael Haftka

University of Florida

H-index: 51
Niklas Linde

Niklas Linde

Université de Lausanne

H-index: 45
Philippe Renard

Philippe Renard

Université de Neuchâtel

H-index: 44
Nam Ho Kim

Nam Ho Kim

University of Florida

H-index: 16
Clément CHEVALIER

Clément CHEVALIER

Université de Neuchâtel

H-index: 8
Tipaluck Krityakierne

Tipaluck Krityakierne

Mahidol University

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