 | Up a level |
Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
Use the mouse wheel or scroll gestures to zoom into the graph.
You can click on the nodes and links to highlight them and move the nodes by dragging them.
Hold down the "Ctrl" key or the "⌘" key while clicking on the nodes to open the list of this person's publications.
Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
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. Available
Openja, M., Laberge, G., & Khomh, F. (2024). Detection and evaluation of bias-inducing features in machine learning. Empirical Software Engineering, 29(1), 71 pages. External link
Laberge, G., Pequignot, Y., Marchand, M., & Khomh, F. (2024, May). Tackling the XAI Disagreement Problem with Regional Explanations [Paper]. 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), Valencia, Spain. External link
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. External link
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). External link
Roy, S., Laberge, G., Roy, B., Khomh, F., Nikanjam, A., & Mondal, S. (2022, October). Why Don't XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. External link
Laberge, G. (2020). Statistical Analysis of Spherical Harmonics Representations of Soil Particles [Master's thesis, Polytechnique Montréal]. Available
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). Available
Laberge, G., Shirzad, S., Diehl, P., Kaiser, H., Prudhomme, S., & Lemoine, A. (2019, November). Scheduling Optimization of Parallel Linear Algebra Algorithms Using Supervised Learning [Paper]. IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC 2019), Denver, CO, USA. External link
Goyette, P.-A., Boulais, E., Normandeau, F., Laberge, G., Juncker, D., & Gervais, T. (2018, November). Reconfigurable multipolar open-space microfluidics [Paper]. 22nd International Conference on Miniaturized Systems for Chemistry and Life Sciences (MicroTAS 2018), Kaohsiung, Taiwan. Unavailable
