Gabriel Laberge

According to our database, Gabriel Laberge authored at least 10 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Detection and evaluation of bias-inducing features in machine learning.

Moses Openja, Gabriel Laberge, Foutse Khomh

Empir. Softw. Eng., February, 2024

Tackling the XAI Disagreement Problem with Regional Explanations.

Gabriel Laberge, Yann Batiste Pequignot, Mario Marchand, Foutse Khomh

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods.

Julien Ferry, Gabriel Laberge, Ulrich Aïvodji

CoRR, 2023

Fooling SHAP with Stealthily Biased Sampling.

Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara, Mario Marchand, Foutse Khomh

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Understanding Interventional TreeSHAP : How and Why it Works.

Gabriel Laberge, Yann Pequignot

CoRR, 2022

Fooling SHAP with Stealthily Biased Sampling.

Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara

CoRR, 2022

How to certify machine learning based safety-critical systems? A systematic literature review.

Florian Tambon, Gabriel Laberge, Le An, Amin Nikanjam, Paulina Stevia Nouwou Mindom, Yann Pequignot, Foutse Khomh, Giulio Antoniol, Ettore Merlo, François Laviolette

Autom. Softw. Eng., 2022

Why Don't XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions.

Saumendu Roy, Gabriel Laberge, Banani Roy, Foutse Khomh, Amin Nikanjam, Saikat Mondal

Proceedings of the IEEE International Conference on Software Maintenance and Evolution, 2022

2021

Partial order: Finding Consensus among Uncertain Feature Attributions.

Gabriel Laberge, Yann Pequignot, Foutse Khomh, Mario Marchand, Alexandre Mathieu

CoRR, 2021

2019

Scheduling Optimization of Parallel Linear Algebra Algorithms Using Supervised Learning.

Gabriel Laberge, Shahrzad Shirzad, Patrick Diehl, Hartmut Kaiser, Serge Prudhomme, Adrian S. Lemoine

Proceedings of the 2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments, 2019