Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations

arXiv:2510.19328v2 Announce Type: replace Abstract: Ensuring that predicted probabilities align with observed frequencies is critical in high-stakes domains such as clinical decision support, autonomous driving and financial risk assessment. Existing calibration methods typically apply a single global transformation or rely on post-hoc binning over predicted confidences, limiting their ability to exploit heterogeneous reliability across sub-populations. We propose Clustered Calibration, a representation-aware framework that identifies sub-populations via clustering in learned feature spaces (e

Source: arXiv cs.LG — read the full report at the original publisher.

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