Decoupled Conformal Optimisation: Efficient Prediction Sets via Independent Tuning and Calibration

• URL: http://arxiv.org/abs/2605.18354v1
• Date: Mon, 18 May 2026 13:10:21 GMT
• Title: Decoupled Conformal Optimisation: Efficient Prediction Sets via Independent Tuning and Calibration
• Abstract summary: We propose Decoupled Conformal optimisation (DCO) as a train-tune-calibrate design principle.<n>DCO uses an independent tuning split for efficiency-oriented structural selection and a fresh calibration split for the final conformal quantile.<n>We show that DCO tracks the nominal coverage level closely while often reducing average prediction-set size or interval width relative to PAC-style calibration.
• Score: 20.21455697379946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian conformal optimisation methods often use the same held-out data both to search for efficient prediction sets and to certify coverage or risk. This coupling is natural for high-probability risk-control guarantees, but it is not necessary when the target is standard finite-sample marginal conformal coverage. We propose Decoupled Conformal Optimisation (DCO), a train-tune-calibrate design principle that uses an independent tuning split for efficiency-oriented structural selection and a fresh calibration split for the final conformal quantile. Conditional on the tuned structure, standard split-conformal exchangeability yields finite-sample marginal coverage for any candidate class, without a confidence parameter or multiple-testing correction. DCO therefore targets a different finite-sample guarantee from PAC-style methods: marginal conformal coverage rather than high-probability risk control. Under consistency assumptions on the coupled risk bound, the two approaches nevertheless converge to the same population threshold. Across classification and regression benchmarks, including ImageNet-A, CIFAR-100, Diabetes, California Housing, and Concrete, DCO tracks the nominal coverage level closely while often reducing average prediction-set size or interval width relative to PAC-style calibration. On ImageNet-A, for example, the average set size decreases from $26.52$ to $25.26$ and the 95th-percentile set size from $58.95$ to $53.73$; on Diabetes, the average interval width decreases from $2.098$ to $1.914$.

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