Explaining Deep Tractable Probabilistic Models: The Sum-Product Network Case
Abstract
We consider the problem of explaining a class of tractable deep probabilistic models, the Sum-Product Networks (SPNs) and present an algorithm ExSPN to generate explanations. To this effect, we define the notion of a context-specific independence tree(CSI-tree) and present an iterative algorithm that converts an SPN to a CSI-tree. The resulting CSI-tree is both interpretable and explainable to the domain expert. We achieve this by extracting the conditional independencies encoded by the SPN and approximating the local context specified by the structure of the SPN. Our extensive empirical evaluations on synthetic, standard, and real-world clinical data sets demonstrate that the CSI-tree exhibits superior explainability.
Cite
Text
Karanam et al. "Explaining Deep Tractable Probabilistic Models: The Sum-Product Network Case." Proceedings of The 11th International Conference on Probabilistic Graphical Models, 2022.Markdown
[Karanam et al. "Explaining Deep Tractable Probabilistic Models: The Sum-Product Network Case." Proceedings of The 11th International Conference on Probabilistic Graphical Models, 2022.](https://mlanthology.org/pgm/2022/karanam2022pgm-explaining/)BibTeX
@inproceedings{karanam2022pgm-explaining,
title = {{Explaining Deep Tractable Probabilistic Models: The Sum-Product Network Case}},
author = {Karanam, Bhagirath Athresh and Mathur, Saurabh and Radivojac, Predrag and Haas, David M and Kersting, Kristian and Natarajan, Sriraam},
booktitle = {Proceedings of The 11th International Conference on Probabilistic Graphical Models},
year = {2022},
pages = {325-336},
volume = {186},
url = {https://mlanthology.org/pgm/2022/karanam2022pgm-explaining/}
}