Post-hoc conformal selection creates a path of selection sets with estimated false discovery proportions, enabling data-driven adaptive FDR control with average reliability guarantees via e-variables and e-BH.
Feedback-Enhanced Online Multiple Testing with Applications to Conformal Selection
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abstract
We study online multiple testing with feedback, where decisions are made sequentially and the true state of the hypothesis is revealed after the decision has been made, either instantly or with a delay. We propose GAIF, a feedback-enhanced generalized alpha-investing framework that dynamically adjusts thresholds using revealed outcomes, ensuring finite-sample false discovery rate (FDR)/marginal FDR control. Extending GAIF to online conformal testing, we construct independent conformal $p$-values and introduce a feedback-driven model selection criterion to identify the best model/score, thereby improving statistical power. We demonstrate the effectiveness of our methods through numerical simulations and real-data applications.
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cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables
Post-hoc conformal selection creates a path of selection sets with estimated false discovery proportions, enabling data-driven adaptive FDR control with average reliability guarantees via e-variables and e-BH.