46 citations · 87 across the 4 of their papers we have counts for
4 papers
Issues with post-hoc counterfactual explanations: a discussion
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala +1
Counterfactual post-hoc interpretability approaches have been proven to be useful tools to generate explanations for the predictions of a trained blackbox classifier. However, the…
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…
Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection
Jonathan Aigrain, Marcin Detyniecki
Despite having excellent performances for a wide variety of tasks, modern neural networks are unable to provide a reliable confidence value allowing to detect misclassifications. T…
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…