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20222026
most citedBenchmarking Bayesian neural networks and evaluation metrics for regression tasks

2 citations · 7 across the 7 of their papers we have counts for

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cs.LG2023★ 2 cited

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…

math.ST2023

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…