46 citations · 54 across the 2 of their papers we have counts for
2 papers
stat.ML2019★ 8 cited
Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees
Xavier Renard, Nicolas Woloszko, Jonathan Aigrain +1
Interpretable surrogates of black-box predictors trained on high-dimensional tabular datasets can struggle to generate comprehensible explanations in the presence of correlated var…
stat.ML2017★ 46 cited
Inverse Classification for Comparison-based Interpretability in Machine Learning
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala +2
In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, nei…