Develops a weighted conformal clustering method that corrects for synthetic labels via conditional distribution shift to achieve finite-sample marginal coverage with explicit bounds for estimated weights.
arXiv preprint arXiv:2310.12964 , 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.
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Weighted Conformal Clustering
Develops a weighted conformal clustering method that corrects for synthetic labels via conditional distribution shift to achieve finite-sample marginal coverage with explicit bounds for estimated weights.
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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.