SP-CCI augments conformal calibration sets with synthetic counterfactual labels and uses RCPS with PPI debiasing to achieve tighter prediction intervals while preserving marginal coverage guarantees.
Springer, 2005
2 Pith papers cite this work. Polarity classification is still indexing.
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ConformaDecompose decomposes conformal prediction uncertainty by progressively localizing calibration sets, revealing reducible epistemic components that align with model limitations across tasks.
citing papers explorer
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Synthetic Counterfactual Labels for Efficient Conformal Counterfactual Inference
SP-CCI augments conformal calibration sets with synthetic counterfactual labels and uses RCPS with PPI debiasing to achieve tighter prediction intervals while preserving marginal coverage guarantees.
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ConformaDecompose: Explaining Uncertainty via Calibration Localization
ConformaDecompose decomposes conformal prediction uncertainty by progressively localizing calibration sets, revealing reducible epistemic components that align with model limitations across tasks.