4 citations · 5 across the 5 of their papers we have counts for
6 papers
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…
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…
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…
Marginal Inference queries in Hidden Markov Models under context-free grammar constraints
Reda Marzouk, Colin de La Higuera
The primary use of any probabilistic model involving a set of random variables is to run inference and sampling queries on it. Inference queries in classical probabilistic models i…
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…
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…