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Mikaël Monet

2 accepted papers

2021

The Tractability of SHAP-Score-Based Explanations for Classification over Deterministic and Decomposable Boolean Circuits

AAAI 2021technical

Scores based on Shapley values are widely used for providing explanations to classification results over machine learning models. A prime example of this is the influential SHAP-score, a version of the Shapley value that can help explain the result of a learned model on a specific entity by assignin…

Cited by 53SourcePDFScholar
2020

Model Interpretability through the lens of Computational Complexity

NeurIPS 2020poster

In spite of several claims stating that some models are more interpretable than others --e.g., "linear models are more interpretable than deep neural networks"-- we still lack a principled notion of interpretability that allows us to formally compare among different classes of models. We make a step…

Cited by 128SourcePDFScholar