Haghtalab et al.
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New research shows exact truthfulness incompatible with sequential prediction calibration
A new paper by Haghtalab et al. explores truthful calibration measures for sequential prediction, building on prior work from 2024. The researchers demonstrate that exact truthfulness in calibration measures is incompat…
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New truthful calibration errors improve multi-class prediction evaluation
Researchers have introduced new methods for measuring calibration errors in multi-class predictions, focusing on the concept of "truthfulness." This means the measurement accurately reflects a predictor's performance wh…
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New calibration measure offers truthful predictions in machine learning
Researchers have introduced a new calibration measure called averaged two-bin calibration error (ATB) designed to be perfectly truthful. This measure quantifies how far a predictor is from perfect calibration and is min…