Derives simultaneous high-probability upper bounds on realized FDP for conformal p-values that hold for arbitrary post-hoc thresholds via an envelope on the null empirical distribution function.
arXiv preprint arXiv:2507.15825 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
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.
GAIF dynamically adjusts testing thresholds with feedback for finite-sample FDR control in sequential settings and extends to conformal selection via feedback-driven model selection.
Introduces SCQ and P-TAMS for structure-adaptive conformal inference under pairwise exchangeability, claiming finite-sample FDR control for large-scale OOD testing.
citing papers explorer
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Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference
Derives simultaneous high-probability upper bounds on realized FDP for conformal p-values that hold for arbitrary post-hoc thresholds via an envelope on the null empirical distribution function.
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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.
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Feedback-Enhanced Online Multiple Testing with Applications to Conformal Selection
GAIF dynamically adjusts testing thresholds with feedback for finite-sample FDR control in sequential settings and extends to conformal selection via feedback-driven model selection.
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Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing
Introduces SCQ and P-TAMS for structure-adaptive conformal inference under pairwise exchangeability, claiming finite-sample FDR control for large-scale OOD testing.