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6 papers · 1 filter
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…
Variable importance for causal forests: breaking down the heterogeneity of treatment effects
Clément Bénard, Julie Josse
Causal random forests provide efficient estimates of heterogeneous treatment effects. However, forest algorithms are also well-known for their black-box nature, and therefore, do n…
Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN
Hamza Boukraichi, Nissrine Akkari, Fabien Casenave +1
The analysis of parametric and non-parametric uncertainties of very large dynamical systems requires the construction of a stochastic model of said system. Linear approaches relyin…
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Clément Bénard, Gérard Biau, Sébastien da Veiga +1
Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools. Shapley effects a…
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA
Clément Bénard, Sébastien da Veiga, Erwan Scornet
Variable importance measures are the main tools to analyze the black-box mechanisms of random forests. Although the mean decrease accuracy (MDA) is widely accepted as the most effi…
Data augmentation and feature selection for automatic model recommendation in computational physics
Thomas Daniel, Fabien Casenave, Nissrine Akkari +1
Classification algorithms have recently found applications in computational physics for the selection of numerical methods or models adapted to the environment and the state of the…