Paper recorded by Signals 4 on 2026-09-16 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17
Category: cs.AI · 人工智能 · first seen 2026-09-17
Formal explainability provides mathematically grounded justifications for individual predictions. However, abductive explanations often exceed human cognitive limits by involving too many features, while probabilistic relaxations have remained largely limited to categorical classification. We present a unified framework for probabilistic explainability based on sparse, anchored linear models, appl