DCO decouples tuning for efficiency from calibration for coverage in conformal prediction, maintaining marginal guarantees and reducing average set sizes on benchmarks like ImageNet-A and Diabetes.
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A classification-integrated conformal framework for zero-inflated outcomes that guarantees marginal coverage and asymptotic minimal length under exchangeability, independent of the underlying models.
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Decoupled Conformal Optimisation: Efficient Prediction Sets via Independent Tuning and Calibration
DCO decouples tuning for efficiency from calibration for coverage in conformal prediction, maintaining marginal guarantees and reducing average set sizes on benchmarks like ImageNet-A and Diabetes.
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Classification-Powered Conformal Inference for Zero-inflated Outcomes
A classification-integrated conformal framework for zero-inflated outcomes that guarantees marginal coverage and asymptotic minimal length under exchangeability, independent of the underlying models.