The relaxation to permutation invariance in distribution is shown to be insufficient for full conformal prediction validity under stochastic non-conformity measures, and Conditional Independence & Permutation Invariance in Distribution is provided as the correct sufficient condition.
arXiv preprint arXiv:2411.17983 , year=
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Introduces target-membership scores and Null-Calibrated Conformal Selection (NCCS) for finite-sample valid FDR control on non-mean-monotone targets.
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.
Introduces SCQ and P-TAMS for structure-adaptive conformal inference under pairwise exchangeability, claiming finite-sample FDR control for large-scale OOD testing.
PH-CS produces a path of conformal selection sets with finite-sample post-hoc FDP estimates so users can maximize a utility balancing selection size and FDR after seeing data.
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Full Conformal Prediction under Stochastic Non-Conformity Measure
The relaxation to permutation invariance in distribution is shown to be insufficient for full conformal prediction validity under stochastic non-conformity measures, and Conditional Independence & Permutation Invariance in Distribution is provided as the correct sufficient condition.
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Null-Calibrated Conformal Selection via Target-Membership Scores
Introduces target-membership scores and Null-Calibrated Conformal Selection (NCCS) for finite-sample valid FDR control on non-mean-monotone targets.
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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
PH-CS produces a path of conformal selection sets with finite-sample post-hoc FDP estimates so users can maximize a utility balancing selection size and FDR after seeing data.
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