← Back to all news
Hugging Face Trending Papers

Conformal Bayes under Label Shift: Post-Hoc Calibration vs. In-Training Adaptation

Read the original on Hugging Face Trending Papers →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

Related stories

arXiv Machine Learning
Sep 14

Split Conformal Prediction with Label-Shift-Adjusted Bayesian Scores

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.

By Hyeonsu Lee, Juyeon Kim, Erkhembayar Jadamba, Seungjin Choi, Hyunjin Shin
More like this →
arXiv Machine Learning
Jul 7

Robust Bayes-Assisted Conformal Prediction

arXiv:2607. 04236v1 Announce Type: cross Abstract: Bayes-assisted conformal prediction combines the strengths of Bayesian modelling with exact, distribution-free frequentist coverage guarantees.

By Kianoosh Ashouritaklimi, Stefano Cortinovis, Fran\c{c}ois Caron
More like this →