9 papers
Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives
Thibaut Vidal, Julien Ferry
Modern machine learning (ML) increasingly relies on complex models whose behavior is difficult to characterize beyond empirical performance metrics. Across a wide range of tasks, i…
When Interpretability Is Unequally Distributed: Fairness in Hybrid Interpretable Models
Ziba Jabbar Zare, Ulrich Aïvodji, Ulrich Aïvodji +2
Hybrid interpretable models combine a transparent component with a black-box model by assigning some examples to the former and deferring the rest to the latter. While this design…
Optimal Counterfactual Search in Tree Ensembles: A Study Across Modeling and Solution Paradigms
Awa Khouna, Youssouf Emine, Julien Ferry +1
Trust in counterfactual explanations depends critically on whether their recommended changes are truly minimal: suboptimal explanations may vastly overshoot the actual changes need…
PACE: Prune-And-Compress Ensemble Models
Fabian Akkerman, Julien Ferry, Théo Guyard +1
Ensemble models achieve state-of-the-art performance on prediction tasks, but usually require aggregating a large number of weak learners. This can hinder deployment, interpretabil…
Counterfactual Maps: What They Are and How to Find Them
Awa Khouna, Julien Ferry, Thibaut Vidal
Counterfactual explanations are a central tool in interpretable machine learning, yet computing them exactly for complex models remains challenging. For tree ensembles, predictions…
Training Set Reconstruction from Differentially Private Forests: How Effective is DP?
Alice Gorgé, Julien Ferry, Sébastien Gambs +1
Recent research has shown that structured machine learning models such as tree ensembles are vulnerable to privacy attacks targeting their training data. To mitigate these risks, d…