Weighted Bayesian Conformal Prediction
Machine learning predictors are rarely deployed on data that match the data they were calibrated on. Conformal prediction and its risk-control generalization promise distribution-free guarantees at deployment, but only under exchangeability or a covariate shift whose likelihood ratio is known exactly -- and the estimated-ratio case, the one that occurs in practice, is explicitly open, out of reach of the Bayesian construction available under exchangeability. We reach it from a different starting point: pushing a Dirichlet process prior on the calibration distribution through the likelihood ratio yields the exact Bayesian posterior over the deployed risk. That posterior is exact given the ...
| Authors | Xiayin Lou, Peng Luo |
|---|---|
| Primary category | cs.LG |
| Published | 7 April 2026 |
| Last updated at source | 9 August 2026 |
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