45 citations · 151 across the 18 of their papers we have counts for
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stat.ML2023
Label Shift Quantification with Robustness Guarantees via Distribution Feature Matching
Bastien Dussap, Gilles Blanchard, Badr-Eddine Chérief-Abdellatif
Quantification learning deals with the task of estimating the target label distribution under label shift. In this paper, we first present a unifying framework, distribution featur…
stat.ML2023★ 3 cited
Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes
Charles Riou, Pierre Alquier, Badr-Eddine Chérief-Abdellatif
Bernstein's condition is a key assumption that guarantees fast rates in machine learning. For example, the Gibbs algorithm with prior has an excess risk in , as oppos…