May 18, 2026 · cs.LGJ/K move · Enter open · S save
Fanyi Wu, Lihua Niu, Samuel Kaski, Michele Caprio
Department of Computer Science, University of Manchester, Manchester, UK · UKRI AI Centre for Doctoral Training in Decision Making for Complex Systems · Department of Computer Science, Aalto University, Espoo, Finland · ELLIS Institute, Finland
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.