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cs.LG2026
SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelism
Reda Marzouk, Shahaf Bassan, Guy Katz
Although Shapley additive explanations (SHAP) can be computed in polynomial time for simple models like decision trees, they unfortunately become NP-hard to compute for more expres…
cs.LG2025
On the Computational Tractability of the (Many) Shapley Values
Reda Marzouk, Shahaf Bassan, Guy Katz +1
Recent studies have examined the computational complexity of computing Shapley additive explanations (also known as SHAP) across various models and distributions, revealing their t…
cs.LG2024
On the Tractability of SHAP Explanations under Markovian Distributions
Reda Marzouk, Colin de La Higuera
Thanks to its solid theoretical foundation, the SHAP framework is arguably one the most widely utilized frameworks for local explainability of ML models. Despite its popularity, it…