Feb. 13, 2024, 5:42 a.m. | Mingda Qiao Letian Zheng

cs.LG updates on arXiv.org arxiv.org

We study a sequential binary prediction setting where the forecaster is evaluated in terms of the calibration distance, which is defined as the $L_1$ distance between the predicted values and the set of predictions that are perfectly calibrated in hindsight. This is analogous to a calibration measure recently proposed by B{\l}asiok, Gopalan, Hu and Nakkiran (STOC 2023) for the offline setting. The calibration distance is a natural and intuitive measure of deviation from perfect calibration, and satisfies a Lipschitz continuity …

binary cs.ds cs.lg prediction predictions set stat.ml study terms values

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