{"ID":2829169,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.13872","arxiv_id":"2512.13872","title":"Measuring Uncertainty Calibration","abstract":"We make two contributions to the problem of estimating the $L_1$ calibration error of a binary classifier from a finite dataset. First, we provide an upper bound for any classifier where the calibration function has bounded variation. Second, we provide a method of modifying any classifier so that its calibration error can be upper bounded efficiently without significantly impacting classifier performance and without any restrictive assumptions. All our results are non-asymptotic and distribution-free. We conclude by providing advice on how to measure calibration error in practice. Our methods yield practical procedures that can be run on real-world datasets with modest overhead.","short_abstract":"We make two contributions to the problem of estimating the $L_1$ calibration error of a binary classifier from a finite dataset. First, we provide an upper bound for any classifier where the calibration function has bounded variation. Second, we provide a method of modifying any classifier so that its calibration error...","url_abs":"https://arxiv.org/abs/2512.13872","url_pdf":"https://arxiv.org/pdf/2512.13872v3","authors":"[\"Kamil Ciosek\",\"Nicolò Felicioni\",\"Sina Ghiassian\",\"Juan Elenter Litwin\",\"Francesco Tonolini\",\"David Gustafsson\",\"Eva Garcia-Martin\",\"Carmen Barcena Gonzalez\",\"Raphaëlle Bertrand-Lalo\"]","published":"2025-12-15T20:03:16Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}