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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.LG2020
On Computability, Learnability and Extractability of Finite State Machines from Recurrent Neural Networks
Reda Marzouk
This work aims at shedding some light on connections between finite state machines (FSMs), and recurrent neural networks (RNNs). Examined connections in this master's thesis is thr…
cs.LG2020
Distance and Equivalence between Finite State Machines and Recurrent Neural Networks: Computational results
Reda Marzouk, Colin de la Higuera
The need of interpreting Deep Learning (DL) models has led, during the past years, to a proliferation of works concerned by this issue. Among strategies which aim at shedding some…