91 citations · 155 across the 11 of their papers we have counts for
8 papers · 1 filter
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",…
Detection and Evaluation of bias-inducing Features in Machine learning
Moses Openja, Gabriel Laberge, Foutse Khomh
The cause-to-effect analysis can help us decompose all the likely causes of a problem, such as an undesirable business situation or unintended harm to the individual(s). This impli…
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods
Julien Ferry, Gabriel Laberge, Ulrich Aïvodji
A hybrid model involves the cooperation of an interpretable model and a complex black box. At inference, any input of the hybrid model is assigned to either its interpretable or co…
Understanding Interventional TreeSHAP : How and Why it Works
Gabriel Laberge, Yann Pequignot
Shapley values are ubiquitous in interpretable Machine Learning due to their strong theoretical background and efficient implementation in the SHAP library. Computing these values…
Fool SHAP with Stealthily Biased Sampling
Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara +2
SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution. Recent studies h…
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set
Gabriel Laberge, Yann Pequignot, Alexandre Mathieu +2
Post-hoc global/local feature attribution methods are progressively being employed to understand the decisions of complex machine learning models. Yet, because of limited amounts o…
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
Florian Tambon, Gabriel Laberge, Le An +7
Context: Machine Learning (ML) has been at the heart of many innovations over the past years. However, including it in so-called 'safety-critical' systems such as automotive or aer…
Scheduling optimization of parallel linear algebra algorithms using Supervised Learning
G. Laberge, S. Shirzad, P. Diehl +3
Linear algebra algorithms are used widely in a variety of domains, e.g machine learning, numerical physics and video games graphics. For all these applications, loop-level parallel…