Modular Conformal Calibration. (arXiv:2206.11468v1 [cs.LG])

Web: http://arxiv.org/abs/2206.11468

Uncertainty estimates must be calibrated (i.e., accurate) and sharp (i.e.,
informative) in order to be useful. This has motivated a variety of methods for
recalibration, which use held-out data to turn an uncalibrated model into a
calibrated model. However, the applicability of existing methods is limited due
to their assumption that the original model is also a probabilistic model. We
introduce a versatile class of algorithms for recalibration in regression that
we call Modular Conformal Calibration (MCC). This framework allows …

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