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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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
ConfSleepNet introduces a conflict-aware evidential aggregation method for multi-modal sleep stage classification using hybrid category structures per modality to produce reliable joint decisions with uncertainty.
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
citing papers explorer
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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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A Conflict-aware Evidential Framework for Reliable Sleep Stage Classification
ConfSleepNet introduces a conflict-aware evidential aggregation method for multi-modal sleep stage classification using hybrid category structures per modality to produce reliable joint decisions with uncertainty.
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Concentration and Calibration in Predictive Bayesian Inference
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.