AI RESEARCH
Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables
arXiv CS.LG
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ArXi:2604.11305v1 Announce Type: new 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). Existing methods fix the target FDR level before observing data, which prevents the user from adapting the balance between number of selected test inputs and FDR to downstream needs and constraints based on the available data.