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:2306.06342 , year=
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C-SymmPI reformulates conditional coverage as miscoverage error over a user-specified function class to deliver near-conditional guarantees under group symmetries and distributional invariance.
The book curates and presents proofs of important existing results in conformal prediction in a unified pedagogical format with illustrations.
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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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Conditional Predictive Inference for General Structured Data with Group Symmetries
C-SymmPI reformulates conditional coverage as miscoverage error over a user-specified function class to deliver near-conditional guarantees under group symmetries and distributional invariance.