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TopicsWeak-to-Strong Generalization Machine Learning Classifier Calibration

In weak-to-strong classification, the models’ maximum calibration errors differ by at most 2√(1−exp(−d(F_w,F_sw))).

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Our theoretical bounds reveal that the calibration of the strong model depends on both the calibration of the weak model and the disagreement between the two models.

The Capabilities and Limitations of Weak-to-Strong Generalization: Generalization and Calibrationpreprintextraction 0.85

Yao, Wei et al. · arXiv.org · Feb 3, 2025 · original

Theorem 4.2: |MCE(F_sw) − MCE(F_w)| ≤ 2·sqrt(1 − exp(−d(F_w,F_sw))).

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