2 citations · 7 across the 7 of their papers we have counts for
8 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…
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-…
PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations
Fabien Casenave, Xavier Roynard, Brian Staber +17
Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows, but their adoption is limited by the lack of large-sca…
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…
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability
Fabien Casenave, Brian Staber, Xavier Roynard
When learning simulations for modeling physical phenomena in industrial designs, geometrical variabilities are of prime interest. While classical regression techniques prove effect…
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization
Clément Bénard, Brian Staber, Sébastien Da Veiga
Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022) for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize…
Benchmarking Bayesian neural networks and evaluation metrics for regression tasks
Brian Staber, Sébastien Da Veiga
Due to the growing adoption of deep neural networks in many fields of science and engineering, modeling and estimating their uncertainties has become of primary importance. Despite…