24 citations · 73 across the 16 of their papers we have counts for
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stat.ML2020★ 16 cited
When OT meets MoM: Robust estimation of Wasserstein Distance
Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi +1
Issued from Optimal Transport, the Wasserstein distance has gained importance in Machine Learning due to its appealing geometrical properties and the increasing availability of eff…
stat.ML2020★ 2 cited
Generalization Bounds in the Presence of Outliers: a Median-of-Means Study
Pierre Laforgue, Guillaume Staerman, Stephan Clémençon
In contrast to the empirical mean, the Median-of-Means (MoM) is an estimator of the mean of a square integrable r.v. , around which accurate nonasymptotic confidence bounds…