Replicable Conformal Prediction
Two analysts who calibrate the same predictive model on independent samples will deploy different prediction sets every time, because the calibration threshold inherits the randomness of the data. Wherever deployments must be audited, cached, or approved across sites, this instability is costly: no one can verify that two calibrations produced the same object. We ask two questions: when can independent calibrations yield the identical classifier, and what must that agreement cost? Perfect agreement is impossible, since a procedure that almost always returns one fixed answer cannot remain valid for every distribution, and exact agreement through shared randomness forces the procedure to ig...
| Authors | Marios Papamichalis, Regina Ruane, Theofanis Papamichalis |
|---|---|
| Primary category | stat.ML |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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