Web: http://arxiv.org/abs/2203.01255

June 17, 2022, 1:11 a.m. | Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao

cs.LG updates on arXiv.org arxiv.org

Introduced as a notion of algorithmic fairness, multicalibration has proved
to be a powerful and versatile concept with implications far beyond its
original intent. This stringent notion -- that predictions be well-calibrated
across a rich class of intersecting subpopulations -- provides its strong
guarantees at a cost: the computational and sample complexity of learning
multicalibrated predictors are high, and grow exponentially with the number of
class labels. In contrast, the relaxed notion of multiaccuracy can be achieved
more efficiently, yet …

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