DPCP delivers end-to-end differentially private conformal prediction sets that are tighter than split-based private methods under the same privacy budget while maintaining coverage under regularity conditions.
Conformal prediction: a unified review of theory and new challenges
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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CMRM adds a conformal quantile regularization on prediction margins to any loss, improving noisy-label classification accuracy up to 3.39% across methods and benchmarks while preserving performance at zero noise.
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Differentially Private Conformal Prediction
DPCP delivers end-to-end differentially private conformal prediction sets that are tighter than split-based private methods under the same privacy budget while maintaining coverage under regularity conditions.
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Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise
CMRM adds a conformal quantile regularization on prediction margins to any loss, improving noisy-label classification accuracy up to 3.39% across methods and benchmarks while preserving performance at zero noise.