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Philipp Terhörst

2 accepted papers

2025

BELIEF - Bayesian Sign Entropy Regularization for LIME Framework

UAI 2025

Explanations of Local Interpretable Model-agnostic Explanations (LIME) are often inconsistent across different runs making them unreliable for eXplainable AI (XAI). The inconsistency stems from sign flips and variability in ranks of the segments for each different run. We propose a Bayesian Regulari

2024

SLICE: Stabilized LIME for Consistent Explanations for Image Classification

CVPR 2024highlight

Local Interpretable Model-agnostic Explanations (LIME) - a widely used post-ad-hoc model agnostic explainable AI (XAI) technique. It works by training a simple transparent (surrogate) model using random samples drawn around the neighborhood of the instance (image) to be explained (IE). Explanations…