Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference
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. This approach fails to provide high-probability bounds on the realized false discovery proportion and invalidates statistical guarantees if the rejection threshold is selected after inspecting the data. This paper establishes ...
| Authors | Ziang Song, Ying Jin, Emmanuel J. Candès |
|---|---|
| Primary category | stat.ME |
| Published | 20 May 2026 |
| Last updated at source | 17 June 2026 |
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