2018/12/12 by Michael Tsang, Tsang, Michael, Youbang Sun +5 · 1 citation
Computer Science · Mathematics · #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Topic Modeling #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1812.04801
arxiv created 2018/12/12 · openalex publication_date 2018/12/12 · arxiv updated 2018/12/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models. We propose Mahé, a novel approach to provide Model-agnostic hierarchical éxplanations of how powerful machine learning models, such as deep neural networks, capture these interactions as either dependent on or free of the context of data instances. Specifically, Mahé provides context-dependent explanations by a novel local interpretation algorithm that effectively captures any-order interactions, and obtains context-free explanations through generalizing context-dependent interactions to explain global behaviors. Experimental results show that Mahé obtains improved local interaction interpretations over state-of-the-art methods and successfully explains interactions that are context-free.