 | Monter d'un niveau |
Goyette, P.-A., Boulais, É., Normandeau, F., Laberge, G., Juncker, D., & Gervais, T. (2019). Microfluidic multipoles theory and applications. Nature Communications, 10(1), 1781 (10 pages). Disponible
Goyette, P.-A., Boulais, E., Normandeau, F., Laberge, G., Juncker, D., & Gervais, T. (novembre 2018). Reconfigurable multipolar open-space microfluidics [Communication écrite]. 22nd International Conference on Miniaturized Systems for Chemistry and Life Sciences (MicroTAS 2018), Kaohsiung, Taiwan. Non 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
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), 50 pages. Lien externe
Laberge, G. (2020). Statistical Analysis of Spherical Harmonics Representations of Soil Particles [Mémoire de maîtrise, Polytechnique Montréal]. Disponible
Laberge, G., Shirzad, S., Diehl, P., Kaiser, H., Prudhomme, S., & Lemoine, A. (novembre 2019). Scheduling Optimization of Parallel Linear Algebra Algorithms Using Supervised Learning [Communication écrite]. IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC 2019), Denver, CO, USA. 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
Roy, S., Laberge, G., Roy, B., Khomh, F., Nikanjam, A., & Mondal, S. (octobre 2022). Why Don't XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. Lien externe
Tambon, F., Laberge, G., An, L., Nikanjam, A., Mindom, P. S. N., Pequignot, Y., Khomh, F., Antoniol, G., Merlo, E., & Laviolette, F. (2022). How to certify machine learning based safety-critical systems? A systematic literature review. Automated Software Engineering, 29(2). Lien externe
