2 citations · 5 across the 16 of their papers we have counts for
6 papers · 1 filter
On Improving Deep Active Learning with Formal Verification
Jonathan Spiegelman, Guy Amir, Guy Katz
Deep Active Learning (DAL) aims to reduce labeling costs in neural-network training by prioritizing the most informative unlabeled samples for annotation. Beyond selecting which sa…
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…
Additive Models Explained: A Computational Complexity Approach
Shahaf Bassan, Michal Moshkovitz, Guy Katz
Generalized Additive Models (GAMs) are commonly considered *interpretable* within the ML community, as their structure makes the relationship between inputs and outputs relatively…
Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations
Shahaf Bassan, Yizhak Yisrael Elboher, Tobias Ladner +2
Despite significant advancements in post-hoc explainability techniques for neural networks, many current methods rely on heuristics and do not provide formally provable guarantees…
What makes an Ensemble (Un) Interpretable?
Shahaf Bassan, Guy Amir, Meirav Zehavi +1
Ensemble models are widely recognized in the ML community for their limited interpretability. For instance, while a single decision tree is considered interpretable, ensembles of t…
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…