1 citations · 3 across the 9 of their papers we have counts for
10 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…
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…
When Interpretability Is Unequally Distributed: Fairness in Hybrid Interpretable Models
Ziba Jabbar Zare, Ulrich Aïvodji, Julien Ferry +1
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…
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…
Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
Fabian Akkerman, Julien Ferry, Christian Artigues +2
Despite their theoretical appeal, totally corrective boosting methods based on linear programming have received limited empirical attention. In this paper, we conduct the first lar…
From Counterfactuals to Trees: Competitive Analysis of Model Extraction Attacks
Awa Khouna, Julien Ferry, Thibaut Vidal
The advent of Machine Learning as a Service (MLaaS) has heightened the trade-off between model explainability and security. In particular, explainability techniques, such as counte…
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…
Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration and Characterization
Lucas Langlade, Julien Ferry, Gabriel Laberge +1
We introduce an enumeration-free method based on mathematical programming to precisely characterize various properties such as fairness or sparsity within the set of "good models",…
Trained Random Forests Completely Reveal your Dataset
Julien Ferry, Ricardo Fukasawa, Timothée Pascal +1
We introduce an optimization-based reconstruction attack capable of completely or near-completely reconstructing a dataset utilized for training a random forest. Notably, our appro…