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.
Least ambiguous set-valued classifiers with bounded error levels
2 Pith papers cite this work. Polarity classification is still indexing.
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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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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 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.