Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation That Includes the Softmax Classifier

MCML Authors

Hannah Laus

→ Group Massimo Fornasier
 Applied Numerical Analysis

Felix Krahmer

Prof. Dr.

Principal Investigator

* Former Principal Investigator

Abstract

Feature-based explanation methods aim to quantify how features influence the model's behavior, either locally or globally, but different methods often disagree, producing conflicting explanations. This disagreement arises primarily from two sources: how feature interactions are handled and how feature dependencies are incorporated. We propose GRANITE, a generalized regional explanation framework that partitions the feature space into regions where interaction and distribution influences are minimized. This approach aligns different explanation methods, yielding more consistent and interpretable explanations. GRANITE unifies existing regional approaches, extends them to feature groups, and introduces a recursive partitioning algorithm to estimate such regions. We demonstrate its effectiveness on real-world datasets, providing a practical tool for consistent and interpretable feature explanations.

misc HLM+26b

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Preprint

May. 2026

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Authors

B. Hayta • H. Laus • S. Mittermaier • F. Krahmer

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Links

arXiv

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Research Area

 A2 | Mathematical Foundations