50 citations · 202 across the 13 of their papers we have counts for
7 papers · 1 filter
Imperceptible Adversarial Attacks on Tabular Data
Vincent Ballet, Xavier Renard, Jonathan Aigrain +3
Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions. While research on the topic has mainly been focusing o…
Fair Adversarial Gradient Tree Boosting
Vincent Grari, Boris Ruf, Sylvain Lamprier +1
Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair cl…
Contract Statements Knowledge Service for Chatbots
Boris Ruf, Matteo Sammarco, Marcin Detyniecki
Towards conversational agents that are capable of handling more complex questions on contractual conditions, formalizing contract statements in a machine readable way is crucial. H…
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala +2
Post-hoc interpretability approaches have been proven to be powerful tools to generate explanations for the predictions made by a trained black-box model. However, they create the…
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…