45 citations · 93 across the 6 of their papers we have counts for
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stat.ML2020★ 45 cited
Interpretable Random Forests via Rule Extraction
Clément Bénard, Gérard Biau, Sébastien da Veiga +1
We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule learning algorithm which takes the form of a short and simple list of rules. State-of-the-art l…
stat.CO2020★ 2 cited
Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients
Sébastien Petit, Julien Bect, Sébastien da Veiga +2
We consider the problem of estimating the parameters of the covariance function of a Gaussian process by cross-validation. We suggest using new cross-validation criteria derived fr…