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stat.ML2025
A reproducible comparative study of categorical kernels for Gaussian process regression, with new clustering-based nested kernels
Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier
Designing categorical kernels is a major challenge for Gaussian process regression with continuous and categorical inputs. Despite previous studies, it is difficult to identify a p…
stat.ML2025
Distributional encoding for Gaussian process regression with qualitative inputs
Sébastien Da Veiga
Gaussian Process (GP) regression is a popular and sample-efficient approach for many engineering applications, where observations are expensive to acquire, and is also a central in…
stat.ML2025
Learning signals defined on graphs with optimal transport and Gaussian process regression
Raphaël Carpintero Perez, Sébastien da Veiga, Josselin Garnier +1
In computational physics, machine learning has now emerged as a powerful complementary tool to explore efficiently candidate designs in engineering studies. Outputs in such supervi…