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LWCal: Loss-Weighted Calibration for Tabular Classifiers with Noisy Calibration Labels
Post-hoc probability calibration is usually evaluated under an optimistic assumption: the held-out calibration labels are clean. In many AI deployment settings, however, labels come from weak annotators, historical decisions, heuristics, or distant supervision, so the same label noise that corrupts
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