Ceci est une ancienne révision du document !
* Uncertainty quantification, Probabilistic machine learning
* Supervised, unsupervised and semi-supervised learning
* Learning from imperfect data, Learning from mixed data
* Active learning, Ensemble learning
*Yonatan Carlos Carranza Alarcón, Cassio de Campos, Sébastien Destercke, Johannes Fürnkranz, Van-Nam Huynh, Eyke Hüllermeier, Mylène Masson, Eneldo Loza Mencía, Michael Rapp, Mohammad Hossein Shaker, Yang Yang.
* Nguyen, V. L., Yang, Y., & de Campos, Cassio P. (2023). Probabilistic Multi-Dimensional Classification. In Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 1-12. 2023.
* Nguyen, V. L., Shaker, M. H., & Hüllermeier, E. (2022). How to measure uncertainty in uncertainty sampling for active learning. Machine Learning, 111(1), 89-122.
* Nguyen, V. L., & Hüllermeier, E. (2021). Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence. Journal of Artificial Intelligence Research, 72, 613-665.
* Nguyen, V. L., Destercke, S., Masson, M. H., & Ghassani, R. (2021). Racing trees to query partial data. Soft Computing, 25(14), 9285-9305.
* Nguyen, V. L., Destercke, S., Masson, M. H., & Hüllermeier, E. (2018). Reliable multi-class classification based on pairwise epistemic and aleatoric uncertainty. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), pp. 5089-5095.
In progress
* Thu-Ha Do: Probabilistic Graphical Models for Complex Learning Tasks (co-supervised with Yves Grandvalet)
Defended
* No
In progress
* No
Defended
* Yang Yang (co-supervised with Cassio de Campos): Generalized Bayesian Network Classifiers
- now PhD student at KU Leuven
Conference Program committees
* AAAI (2021, 2023)
* AISTATS (2021-2022)
* UAI (2021)
* ISIPTA (2023)
* IUKM (2023)
Conference/workshop organization committees
* SMPS/BELIEF (2018)
* WUML & WPMSIIP (2017)
Reviewing activities
* International Journal of Approximate Reasoning (2023), Pattern Recognition (2023)