Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision. (arXiv:2304.06158v2 [stat.ME] UPDATED)

Conformal inference has played a pivotal role in providing uncertainty
quantification for black-box ML prediction algorithms with finite sample
guarantees. Traditionally, conformal prediction inference requires a
data-independent specification of miscoverage level. In practical applications,
one might want to update the miscoverage level after computing the prediction
set. For example, in the context of binary classification, the analyst might
start with a $95\%$ prediction sets and see that most prediction sets contain
all outcome classes. Prediction sets with both classes being …

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