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

June 24, 2022, 1:11 a.m. | Charles Marx, Shengjia Zhou, Willie Neiswanger, Stefano Ermon

stat.ML updates on arXiv.org arxiv.org

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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