Hugging Face Trending Papers June 10, 2026

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.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning

Jun 11

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

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.

By Seungjin Choi

arXiv Machine Learning

Jun 11

Coverage Guarantees for Pseudo-Calibrated Conformal Prediction under Distribution Shift

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.

By Farbod Siahkali, Ashwin Verma, Vijay Gupta

fine-tuning

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

arXiv Machine Learning

Jun 16

Conformal Candidate Certification for Offline Model-Based Optimization

arXiv:2606. 15217v1 Announce Type: cross Abstract: Offline model-based optimization (MBO) proposes candidates by optimizing a surrogate trained on a fixed historical dataset.

By Seungjin Choi

arXiv Machine Learning

Jul 14

Online conformal inference with retrospective adjustment for faster adaptation to distribution shift

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.

By Jungbin Jun, Ilsang Ohn

arXiv Machine Learning

Jul 20

Improving Backward Conformal Prediction via Non-Conformity Score Transformation

arXiv:2602. 01733v3 Announce Type: replace-cross Abstract: Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guarantees.

By Junxian Liu, Hao Zeng, Hongxin Wei

benchmarks

Related stories

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

Coverage Guarantees for Pseudo-Calibrated Conformal Prediction under Distribution Shift

Robust Bayes-Assisted Conformal Prediction

Conformal Candidate Certification for Offline Model-Based Optimization

Online conformal inference with retrospective adjustment for faster adaptation to distribution shift

Improving Backward Conformal Prediction via Non-Conformity Score Transformation