Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows

• URL: http://arxiv.org/abs/2512.00835v1
• Date: Sun, 30 Nov 2025 11:08:40 GMT
• Title: Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows
• Authors: Adriel Sosa Marco, John Daniel Kirwan, Alexia Toumpa, Simos Gerasimou,
• Abstract summary: We introduce MCNF, a novel uncertainty quantification method that produces both prediction intervals and the full conditioned predictive distribution.<n>MCNF operates on top of the underlying trained predictive model; thus, no predictive model retraining is needed.<n>We provide experimental evidence that the MCNF-based uncertainty estimate is well calibrated, is competitive with state-of-the-art uncertainty quantification methods, and provides richer information for downstream decision-making tasks.
• Score: 1.8899300124593648
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Quantifying uncertainty in deep regression models is important both for understanding the confidence of the model and for safe decision-making in high-risk domains. Existing approaches that yield prediction intervals overlook distributional information, neglecting the effect of multimodal or asymmetric distributions on decision-making. Similarly, full or approximated Bayesian methods, while yielding the predictive posterior density, demand major modifications to the model architecture and retraining. We introduce MCNF, a novel post hoc uncertainty quantification method that produces both prediction intervals and the full conditioned predictive distribution. MCNF operates on top of the underlying trained predictive model; thus, no predictive model retraining is needed. We provide experimental evidence that the MCNF-based uncertainty estimate is well calibrated, is competitive with state-of-the-art uncertainty quantification methods, and provides richer information for downstream decision-making tasks.

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