AI RESEARCH
Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference
arXiv CS.LG
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ArXi:2605.20726v1 Announce Type: cross Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-values fall below a threshold. The quality of such methods is often measured by the false discovery proportion (FDP), defined as the fraction of incorrect selections. Existing approaches typically control the expected value of the FDP, using methods such as the Benjamini-Hochberg procedure.