38 citations · 80 across the 9 of their papers we have counts for
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cs.AI2021★ 5 cited
Foundations of Symbolic Languages for Model Interpretability
Marcelo Arenas, Daniel Baez, Pablo Barceló +2
Several queries and scores have recently been proposed to explain individual predictions over ML models. Given the need for flexible, reliable, and easy-to-apply interpretability m…
cs.AI2021★ 8 cited
On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results
Marcelo Arenas, Pablo Barceló, Leopoldo Bertossi +1
In Machine Learning, the -score is a version of the Shapley value that is used to explain the result of a learned model on a specific entity by assigning a score to…