Learning Optimal Conformal Classifiers. (arXiv:2110.09192v3 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2110.09192

Modern deep learning based classifiers show very high accuracy on test data
but this does not provide sufficient guarantees for safe deployment, especially
in high-stake AI applications such as medical diagnosis. Usually, predictions
are obtained without a reliable uncertainty estimate or a formal guarantee.
Conformal prediction (CP) addresses these issues by using the classifier's
predictions, e.g., its probability estimates, to predict confidence sets
containing the true class with a user-specified probability. However, using CP
as a separate processing step after …

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