68 citations
- Centre de Recherche en Économie et StatistiqueFR25 papers
- ENSAE ParisFR22 papers
- Institut de recherche mathématique de RennesFR11 papers
- Centre for Research in Engineering Surface TechnologyIE10 papers
- Centre National de la Recherche ScientifiqueFR10 papers
- Université de RennesFR10 papers
- Institut National des Sciences Appliquées de RennesFR3 papers
- Orange (France)FR3 papers
- Université Paris-SaclayFR3 papers
- Astrophysique, Instrumentation et ModélisationFR2 papers
- Bocconi UniversityIT2 papers
- Département mathématiques, informatique, sciences de la donnée et technologies du numériqueFR2 papers
8 papers · 1 filter
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…
Asymptotics of SGD in Sequence-Single Index Models and Single-Layer Attention Networks
Luca Arnaboldi, Bruno Loureiro, Ludovic Stephan +2
We study the dynamics of stochastic gradient descent (SGD) for a class of sequence models termed Sequence Single-Index (SSI) models, where the target depends on a single direction…
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…
A Quadrature Rule combining Control Variates and Adaptive Importance Sampling
Rémi Leluc, François Portier, Johan Segers +1
Driven by several successful applications such as in stochastic gradient descent or in Bayesian computation, control variates have become a major tool for Monte Carlo integration.…
Model-based Clustering with Missing Not At Random Data
Aude Sportisse, Matthieu Marbac, Fabien Laporte +4
Model-based unsupervised learning, as any learning task, stalls as soon as missing data occurs. This is even more true when the missing data are informative, or said missing not at…