5 papers
Asymmetric conformal prediction with penalized kernel sum-of-squares
Louis Allain, Sébastien Da Veiga, Brian Staber
Conformal prediction (CP) is a distribution-free method to construct reliable prediction intervals that has gained significant attention in recent years. Despite its success and va…
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…
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…
Scalable and adaptive prediction bands with kernel sum-of-squares
Louis Allain, Sébastien da Veiga, Brian Staber
Conformal Prediction (CP) is a popular framework for constructing prediction bands with valid coverage in finite samples, while being free of any distributional assumption. A well-…
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…