3 papers · 1 filter
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…
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…
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Raphaël Carpintero Perez, Sébastien da Veiga, Josselin Garnier +1
Supervised learning has recently garnered significant attention in the field of computational physics due to its ability to effectively extract complex patterns for tasks like solv…