Conformal Prediction via Transported Beta Laws
Split conformal prediction provides finite-sample marginal coverage under exchangeability, but this guarantee averages over the random calibration sample. We study instead the law of the calibration-conditional coverage induced by a realized conformal threshold. In the continuous i.i.d. setting this law is exactly $Beta(k,n+1-k)$, so the usual marginal guarantee corresponds to its mean. We take this beta law as a finite-sample reference object and quantify departures from it using Wasserstein distances on $[0,1]$. The framework yields direct bounds on marginal coverage gaps and on bad-calibration probabilities, and separates different sources of non-i.i.d. behavior according to how they d...
| Authors | Thiago R. Ramos, Helton Graziadei, Luben M. C. Cabezas |
|---|---|
| Primary category | stat.ML |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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