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Author

David Ginsbourger

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2 papers indexed here

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Open access Aug 2026

Gaussian Process Modeling with Genotype $$\times $$ Environment Kernels for Wheat Performance Prediction

Optimizing wheat variety selection for high performance in different environmental conditions is critical for reliable food production and stable incomes for growers. We employ a statistical machine learning framework utilizing Gaussian process (GP) models to capture the effects of genetic and environmental factors...

Lea Friedli, Tim Steinert, Nathalie Wuyts et al. · 0 citations
#machine learning Preprint Sep 2026

Triply-Scalable Equivariant Gaussian Process Modeling

A matrix-free equivariant full-GP implementation that combines an exact Kronecker reduction with preconditioned conjugate-gradient solves is developed, enabling fast and scalable evaluation of the full joint predictive density, and triply scalable equivariant Gaussian processes are introduced.

Tim Steinert, David Ginsbourger · 0 citations

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