Ziyi Liang, Matteo Sesia, Wenguang Sun

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

  • Statistics, Probability and Uncertainty
  • Statistics and Probability
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Abstract 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.

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