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Efficiency criteria for conformal prediction, such as observed fuzziness (i.e., the sum of p-values associated with false labels), are commonly used to evaluate the performance of given conformal predictors. Here, we investigate whether it is possible to exploit efficiency criteria to learn classifiers, both conformal predictors and point classifiers, by using such criteria as training objective functions. The proposed idea is implemented for the problem of binary classification of hand-writtenarXiv:2005.07037v1 fatcat:mycwe2ylq5gztbogfurjtzehn4