Conformal Bayes under Label Shift: Post-Hoc Calibration vs. In-Training Adaptation
Conformal Bayes combines Bayesian posterior predictives with conformal calibration to produce prediction sets that are both statistically valid and geometrically efficient. We study conformal Bayes under label shift from a unified perspective, identifying two complementary approaches that restore nominal target-domain coverage through importance-weighted conformal calibration but operate through independent mechanisms. \emph{Post-hoc calibration} tilts the posterior predictive toward the target domain and corrects the conformal threshold via an importance-weighted quantile, leaving the parameter posterior unchanged. \emph{In-training adaptation} tilts the parameter posterior itself to the...
| Authors | Seungjin Choi |
|---|---|
| Primary category | stat.ML |
| Published | 10 June 2026 |
| Last updated at source | 26 June 2026 |
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