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.
Distribution-free predictive inference for regression
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
Novel methods for valid conformal prediction after data-dependent model selection without additional sample splitting, with finite-sample guarantees and asymptotic optimality under regularity conditions.
An approximate inequality for the probability involving order statistics under near-i.i.d. conditions is established and applied to justify resampling-based statistical procedures.
citing papers explorer
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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.
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Conformal prediction after data-dependent model selection
Novel methods for valid conformal prediction after data-dependent model selection without additional sample splitting, with finite-sample guarantees and asymptotic optimality under regularity conditions.
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On a Probability Inequality for Order Statistics with Applications to Bootstrap, Conformal Prediction, and more
An approximate inequality for the probability involving order statistics under near-i.i.d. conditions is established and applied to justify resampling-based statistical procedures.