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Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
Example on task completion times stressing the importance of distribution-free intervals, as well as .py file containing code to calculate such intervals.
Conformal prediction from scratch: the exact finite-sample coverage law, the 2012 deployment that broke it (Hurricane Sandy included), and the online repair
A distribution-free conformal prediction certification layer for safety-critical ordinal classification. Guarantees contiguous prediction sets, marginal/group-conditional validity, true-label coverage under banded reporting noise, and severity-weighted risk control (fatal-omission bounds) with an explicit additive slack budget.