arXiv Machine Learning June 3, 2026 By Nabil Alami, Jad Zakharia, Souhaib Ben Taieb

Set-Preserving Calibration from Conformal P-Values to E-Values

Read the original on arXiv Machine Learning →

arXiv:2606. 03600v1 Announce Type: cross Abstract: Standard conformal prediction (CP) procedures are typically formulated in terms of p-values, but reliance on p-values alone limits flexibility, for example, when combining dependent evidence across models or data splits.

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 arXiv Machine Learning.

arXiv Machine Learning

Jul 21

Isotonic Conformal Prediction

arXiv:2607. 16675v1 Announce Type: cross Abstract: A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use in downstream decision-making.

By Daniel Bensimon, Sean Xiang Yu, Eric D. Kolaczyk, Archer Y. Yang

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

arXiv Machine Learning

Jul 1

Accelerating Conformal Prediction via Approximate Leave-One-Out

arXiv:2606. 31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost.

By Jiachen Cong, Jingbo Liu

arXiv Machine Learning

Sep 15

Noise-Adaptive Conformal Classification with Marginal Coverage

arXiv:2501.18060v2 Announce Type: replace-cross Abstract: Conformal inference provides a rigorous statistical framework for uncertainty quantification in machine learning, enabling well-calibrated pr...

By Teresa Bortolotti, Y. X. Rachel Wang, Xin Tong, Alessandra Menafoglio, Simone Vantini, Matteo Sesia

arXiv Machine Learning

Jun 17

Questioning the Coverage-Length Metric in Conformal Prediction: When Shorter Intervals Are Not Better

arXiv:2601. 21455v2 Announce Type: replace-cross Abstract: Conformal prediction(CP) has become a cornerstone of distribution-free uncertainty quantification, conventionally evaluated by its coverage and interval length.

By Yizhou Min, Yizhou Lu, Lanqi Li, Zhen Zhang, Jiaye Teng

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

One email a morning, machine-written

Related stories

Isotonic Conformal Prediction

Improving Backward Conformal Prediction via Non-Conformity Score Transformation

Accelerating Conformal Prediction via Approximate Leave-One-Out

Noise-Adaptive Conformal Classification with Marginal Coverage

Questioning the Coverage-Length Metric in Conformal Prediction: When Shorter Intervals Are Not Better

Coverage Guarantees for Pseudo-Calibrated Conformal Prediction under Distribution Shift