Integrative conformal p-values for out-of-distribution testing with labelled outliers

• 成果类型: Article
• 署名作者: Liang, Ziyi; Sesia, Matteo; Sun, Wenguang
• 署名单位: University of Southern California; University of Southern California; Zhejiang University; Zhejiang University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkad138
• 发表日期: 2024
• 页码: 671-693
• 关键词: false discovery rate; predictive inference; Novelty Detection; CLASSIFICATION

摘要

This paper presents a conformal inference method for out-of-distribution testing that leverages side information from labelled outliers, which are commonly underutilized or even discarded by conventional conformal p-values. This solution is practical and blends inductive and transductive inference strategies to adaptively weight conformal p-values, while also automatically leveraging the most powerful model from a collection of one-class and binary classifiers. Further, this approach leads to rigorous false discovery rate control in multiple testing when combined with a conditional calibration strategy. Extensive numerical simulations show that the proposed method outperforms existing approaches.