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stat.ML2022★ 4 cited
Bounding and Approximating Intersectional Fairness through Marginal Fairness
Mathieu Molina, Patrick Loiseau
Discrimination in machine learning often arises along multiple dimensions (a.k.a. protected attributes); it is then desirable to ensure \emph{intersectional fairness} -- i.e., that…
cs.LG2022
On Preemption and Learning in Stochastic Scheduling
Nadav Merlis, Hugo Richard, Flore Sentenac +3
We study single-machine scheduling of jobs, each belonging to a job type that determines its duration distribution. We start by analyzing the scenario where the type characteristic…