Feb. 13, 2024, 5:43 a.m. | Parikshit Gopalan Lunjia Hu Guy N. Rothblum

cs.LG updates on arXiv.org arxiv.org

Consider a multi-class labelling problem, where the labels can take values in $[k]$, and a predictor predicts a distribution over the labels. In this work, we study the following foundational question: Are there notions of multi-class calibration that give strong guarantees of meaningful predictions and can be achieved in time and sample complexities polynomial in $k$? Prior notions of calibration exhibit a tradeoff between computational efficiency and expressivity: they either suffer from having sample complexity exponential in $k$, or needing …

class complexities cs.cc cs.ds cs.lg distribution labelling labels math.st polynomial predictions question sample stat.ml stat.th study values work

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