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The Calibration Generalization Gap. (arXiv:2210.01964v2 [cs.LG] UPDATED)
Oct. 7, 2022, 1:13 a.m. | A. Michael Carrell, Neil Mallinar, James Lucas, Preetum Nakkiran
cs.LG updates on arXiv.org arxiv.org
Calibration is a fundamental property of a good predictive model: it requires
that the model predicts correctly in proportion to its confidence. Modern
neural networks, however, provide no strong guarantees on their calibration --
and can be either poorly calibrated or well-calibrated depending on the
setting. It is currently unclear which factors contribute to good calibration
(architecture, data augmentation, overparameterization, etc), though various
claims exist in the literature.
We propose a systematic way to study the calibration error: by decomposing …
More from arxiv.org / cs.LG updates on arXiv.org
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