Published in AISTATS, 2024
Compute explanations on regions defined by a decision tree
Recommended citation: Laberge, G., Pequignot, Y., Marchand, M., & Khomh, F. (2024, May). Tackling the XAI Disagreement Problem with Regional Explanations. In International Conference on Artificial Intelligence and Statistics (AISTATS) (Vol. 238).
Published in ICLR, 2023
Manipulating Shapley values by cherry-picking the reference samples
Recommended citation: Laberge, G., Aïvodji, U., Hara, S., Marchand, M., & Khomh, F. (2023, May). Fooling SHAP with Stealthily Biased Sampling. In The Eleventh International Conference on Learning Representations.
Published in TMLR, 2024
Explore the Transparency-Accuracy tradeoff
Recommended citation: Ferry, J., Laberge, G., Aivodji, U. (2024). Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods. Transactions on Machine Learning Research, 2835-8856.
Published in Journal of Machine Learning Research, 2023
Computing the consensus of local/global feature importance across all models in the Rashomon Set
Recommended citation: Laberge, G., Pequignot, Y., Mathieu, A., Khomh, F., & Marchand, M. (2023). Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set. Journal of Machine Learning Research, 24(364), 1-50.
Published in Automated Software Engineering, 2022
A review on the challenges of certifying machine learning components in critical systems.
Recommended citation: Tambon, F., Laberge, G., An, L., Nikanjam, A., Mindom, P. S. N., Pequignot, Y., ... & Laviolette, F. (2022). How to certify machine learning based safety-critical systems? A systematic literature review. Automated Software Engineering, 29(2), 38.