1 citations · 2 across the 3 of their papers we have counts for
4 papers
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…
The linear sampling method for data generated by small random scatterers
J. Garnier, H. Haddar, H. Montanelli
We present an extension of the linear sampling method for solving the sound-soft inverse scattering problem in two dimensions with data generated by randomly distributed small scat…