324 citations · 484 across the 31 of their papers we have counts for
13 papers · 1 filter
CRPS-Based Targeted Sequential Design with Application in Chemical Space
Lea Friedli, Athénaïs Gautier, Anna Broccard +1
Sequential design of real and computer experiments via Gaussian Process (GP) models has proven useful for parsimonious, goal-oriented data acquisition purposes. In this work, we fo…
Efficient pooling of predictions via kernel embeddings
Sam Allen, David Ginsbourger, Johanna Ziegel
Probabilistic predictions are probability distributions over the set of possible outcomes. Such predictions quantify the uncertainty in the outcome, making them essential for effec…
Non-Sequential Ensemble Kalman Filtering using Distributed Arrays
Cédric Travelletti, Jörg Franke, David Ginsbourger +1
This work introduces a new, distributed implementation of the Ensemble Kalman Filter (EnKF) that allows for non-sequential assimilation of large datasets in high-dimensional proble…
Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures
Johanna Ziegel, David Ginsbourger, Lutz Dümbgen
We present new classes of positive definite kernels on non-standard spaces that are integrally strictly positive definite or characteristic. In particular, we discuss radial kernel…
Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors
Cédric Travelletti, David Ginsbourger, Niklas Linde
We consider the use of Gaussian process (GP) priors for solving inverse problems in a Bayesian framework. As is well known, the computational complexity of GPs scales cubically in…
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization
Poompol Buathong, David Ginsbourger, Tipaluck Krityakierne
We focus on kernel methods for set-valued inputs and their application to Bayesian set optimization, notably combinatorial optimization. We investigate two classes of set kernels t…