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cs.AI2026
Beyond Shapley: Efficient Computation of Asymmetric Shapley Values
Ezequiel Companeetz, Santiago Cifuentes, Sergio Abriola
We address the problem of explainability in machine learning models through feature attribution methods. In particular, we consider a variant of Shapley values known as Asymmetric…
cs.AI2026
General Agents Contain World Models, even under Partial Observability and Stochasticity
Santiago Cifuentes
Deciding whether an agent possesses a model of its surrounding world is a fundamental step toward understanding its capabilities and limitations. In [10], it was shown that, within…
cs.AI2025
Feature Relevancy, Necessity and Usefulness: Complexity and Algorithms
Tomás Capdevielle, Santiago Cifuentes
Given a classification model and a prediction for some input, there are heuristic strategies for ranking features according to their importance in regard to the prediction. One com…