Multiclass Local Calibration with the Jensen-Shannon Distance

ArXi:2510.26566v2 Announce Type: replace-cross Developing trustworthy Machine Learning (ML) models requires their predicted probabilities to be well-calibrated, meaning they should reflect true-class frequencies. Among calibration notions in multiclass classification, strong calibration is the most stringent, as it requires all predicted probabilities to be simultaneously calibrated across all classes.