arXiv:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).
By Meiyi Zhu, Osvaldo Simeone
arXiv:2402. 16388v4 Announce Type: replace-cross Abstract: The need for uncertainty quantification in anomaly detection systems has become increasingly important.
By Oliver Hennh\"ofer, Christine Preisach
arXiv:2601. 02610v3 Announce Type: replace-cross Abstract: Novelty detection via conformal $p$-values and BH procedure provides distribution-free global false discovery rate (FDR) control.
By Zijun Gao, Etienne Roquain, Daniel Xiang
arXiv:2607. 22985v1 Announce Type: cross Abstract: Conformalized selection has been widely applied to select high-quality candidates from large datasets with rigorous uncertainty quantification, such as reliable labeling, drug discovery, and the alignment of large language models.
By Chengyao Yu, Hongxin Wei, Bingyi Jing
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.
By Nabil Alami, Jad Zakharia, Souhaib Ben Taieb
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