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arXiv · 2505.02277

Epistemic Wrapping for Uncertainty Quantification

Maryam Sultana·Neil Yorke-Smith·Kaizheng Wang·Shireen Kudukkil Manchingal·Muhammad Mubashar·Fabio Cuzzolin
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Abstract

Uncertainty estimation is pivotal in machine learning, especially for classification tasks, as it improves the robustness and reliability of models. We introduce a novel `Epistemic Wrapping' methodology aimed at improving uncertainty estimation in classification. Our approach uses Bayesian Neural Networks (BNNs) as a baseline and transforms their outputs into belief function posteriors, effectively capturing epistemic uncertainty and offering an efficient and general methodology for uncertainty quantification. Comprehensive experiments employing a Bayesian Neural Network (BNN) baseline and an Interval Neural Network for inference on the MNIST, Fashion-MNIST, CIFAR-10 and CIFAR-100 datasets demonstrate that our Epistemic Wrapper significantly enhances generalisation and uncertainty quantification.

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Maryam Sultana, Neil Yorke-Smith, Kaizheng Wang, Shireen Kudukkil Manchingal, Muhammad Mubashar, Fabio Cuzzolin. 2025-05-04. Epistemic Wrapping for Uncertainty Quantification. https://arxiv.org/abs/2505.02277

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