Credal Ensemble Distillation for Uncertainty Quantification

• URL: http://arxiv.org/abs/2511.13766v1
• Date: Fri, 14 Nov 2025 14:53:42 GMT
• Title: Credal Ensemble Distillation for Uncertainty Quantification
• Authors: Kaizheng Wang, Fabio Cuzzolin, David Moens, Hans Hallez,
• Abstract summary: We propose credal ensemble distillation (CED), a framework that compresses a deep ensemble into a single model, CREDIT, for classification tasks.<n>CED achieves superior or comparable uncertainty estimation compared to several existing baselines, while substantially reducing inference overhead compared to deep ensembles.
• Score: 12.36665123584814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep ensembles (DE) have emerged as a powerful approach for quantifying predictive uncertainty and distinguishing its aleatoric and epistemic components, thereby enhancing model robustness and reliability. However, their high computational and memory costs during inference pose significant challenges for wide practical deployment. To overcome this issue, we propose credal ensemble distillation (CED), a novel framework that compresses a DE into a single model, CREDIT, for classification tasks. Instead of a single softmax probability distribution, CREDIT predicts class-wise probability intervals that define a credal set, a convex set of probability distributions, for uncertainty quantification. Empirical results on out-of-distribution detection benchmarks demonstrate that CED achieves superior or comparable uncertainty estimation compared to several existing baselines, while substantially reducing inference overhead compared to DE.

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