1 citations · 3 across the 9 of their papers we have counts for
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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",…