4 citations · 4 across the 3 of their papers we have counts for
4 papers
Learning Model Agnostic Explanations via Constraint Programming
Frederic Koriche, Jean-Marie Lagniez, Stefan Mengel +1
Interpretable Machine Learning faces a recurring challenge of explaining the predictions made by opaque classifiers such as ensemble models, kernel methods, or neural networks in t…
On the Explanatory Power of Decision Trees
Gilles Audemard, Steve Bellart, Louenas Bounia +3
Decision trees have long been recognized as models of choice in sensitive applications where interpretability is of paramount importance. In this paper, we examine the computationa…
Trading Complexity for Sparsity in Random Forest Explanations
Gilles Audemard, Steve Bellart, Louenas Bounia +3
Random forests have long been considered as powerful model ensembles in machine learning. By training multiple decision trees, whose diversity is fostered through data and feature…
On the Computational Intelligibility of Boolean Classifiers
Gilles Audemard, Steve Bellart, Louenas Bounia +3
In this paper, we investigate the computational intelligibility of Boolean classifiers, characterized by their ability to answer XAI queries in polynomial time. The classifiers und…