A0242
Title: Integrative conformal p-values for out-of-distribution testing with labeled outliers
Authors: Ziyi Liang - University of California, Irvine (United States) [presenting]
Abstract: The focus is on presenting 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. The 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, the approach leads to rigorous false discovery rate control in multiple tests when combined with a conditional calibration strategy. Extensive numerical simulations show the proposed method outperforms existing approaches.
