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Laberge, G. (2024). Trustworthy Additive Explanations of Machine Learning Models through Increased Alignment [Thèse de doctorat, Polytechnique Montréal]. Disponible
Laberge, G., Pequignot, Y., Marchand, M., & Khomh, F. (mai 2024). Tackling the XAI Disagreement Problem with Regional Explanations [Communication écrite]. 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), Valencia, Spain. Lien externe
Openja, M., Laberge, G., & Khomh, F. (2024). Detection and evaluation of bias-inducing features in machine learning. Empirical Software Engineering, 29(1), 71 pages. Lien externe
Oueslati, K., Laberge, G., Lamothe, M., & Khomh, F. (2024). Mining Action Rules for Defect Reduction Planning. Proceedings of the ACM on Software Engineering, 1(FSE), 2309-2331. Disponible
