Search Results for author: Mikaël Monet

Found 3 papers, 0 papers with code

On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results

no code implementations16 Apr 2021 Marcelo Arenas, Pablo Barceló, Leopoldo Bertossi, Mikaël Monet

While in general computing Shapley values is an intractable problem, we prove a strong positive result stating that the $\mathsf{SHAP}$-score can be computed in polynomial time over deterministic and decomposable Boolean circuits.

Model Interpretability through the Lens of Computational Complexity

no code implementations NeurIPS 2020 Pablo Barceló, Mikaël Monet, Jorge Pérez, Bernardo Subercaseaux

We prove that this notion provides a good theoretical counterpart to current beliefs on the interpretability of models; in particular, we show that under our definition and assuming standard complexity-theoretical assumptions (such as P$\neq$NP), both linear and tree-based models are strictly more interpretable than neural networks.

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

no code implementations28 Jul 2020 Marcelo Arenas, Pablo Barceló Leopoldo Bertossi, Mikaël Monet

While in general computing Shapley values is a computationally intractable problem, it has recently been claimed that the SHAP-score can be computed in polynomial time over the class of decision trees.

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