arXiv:2606. 11865v1 Announce Type: cross Abstract: Conformal Bayes combines Bayesian posterior predictives with conformal calibration to produce prediction sets that are both statistically valid and geometrically efficient.
arXiv:2609. 27976v1 Announce Type: cross Abstract: Conformal Bayes combines Bayesian posterior predictive scores with conformal calibration, but under continuous label shift both the score and calibration weight depend on the unknown response-marginal density ratio.
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.
arXiv:2602. 14913v2 Announce Type: replace Abstract: Conformal prediction (CP) offers distribution-free marginal coverage guarantees under an exchangeability assumption, but these guarantees can fail if the data distribution shifts.
arXiv:2511. 04275v2 Announce Type: replace-cross Abstract: Conformal prediction has emerged as a powerful framework for constructing distribution-free prediction sets with guaranteed coverage assuming only the exchangeability assumption.
arXiv:2608. 00701v1 Announce Type: cross Abstract: Reweighting source samples to match a target covariate distribution is a standard response to distribution shift when generalizing evidence from one population to another.
The paper introduces the Label-Shift-Adjusted Bayesian Score (LSA score), a nonconformity measure for conformal prediction that corrects Bayesian scores under label shift by applying an importance-weighted transformation of the source predictive distribution. Unlike residual-based scores that produce uniform-width intervals, the LSA score yields shorter, adaptive intervals while maintaining comparable coverage in the target domain. Experiments on molecular property prediction demonstrate that the LSA score outperforms both residual-based and source-based Bayesian scores, though all methods experience some coverage loss under stronger shifts due to density-ratio estimation challenges.
Conformal Calibration Transfer addresses the challenge of applying conformal prediction when labeled calibration data is only available in a source space, while predictions are needed in a target space linked via unlabeled paired observations. The proposed Transported Conformal Calibration (TCC) method transports source calibration into the target domain and then corrects residual mismatches using only unlabeled target inputs, with two variants: TCC‑KS, which conservatively adjusts calibration based on a label‑free uncertainty surrogate, and weighted‑TCC, which reweights transported calibration for efficiency when weights are stable. Finite‑sample target‑domain coverage guarantees are provided, and experiments on CIFAR‑100‑C, Tiny‑ImageNet‑C, and SEN12MS demonstrate reliable coverage transfer without labeled target data, along with label‑free diagnostics to signal when correction is required.
arXiv:2609.10333v1 Announce Type: new Abstract: Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-fr...
arXiv:2608. 01460v1 Announce Type: new Abstract: Conformal prediction (CP) is a distribution-free framework for uncertainty quantification that has recently been adapted to large language models (LLMs), providing prediction sets with finite-sample coverage guarantees under exchangeability.
Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-free coverage guarantees. However, standard conform...
arXiv:2606. 14909v1 Announce Type: cross Abstract: We consider the problem of uncertainty quantification for a pretrained classification model deployed under unknown distribution shift.