Sharp and Stable Label-Free Calibration Certificates from Logical Coherence
Read the original on arXiv Machine Learning →The paper presents a framework for deriving sharp, label‑free calibration certificates for query‑conditioned probabilistic predictors. It establishes minimax lower bounds on calibration residuals under various betting restrictions, shows that these bounds can be discontinuous and that Lipschitz stakes yield stable, sharp bounds. The authors also demonstrate practical algorithms—projected online gradient ascent and anytime‑valid betting—for generating fixed‑horizon and anytime‑valid certificates, and validate the approach on a preregistered study of four predictors across two datasets.
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