UMR CNRS 7253

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Research interests

* Uncertainty quantification, Probabilistic machine learning
* Supervised, unsupervised and semi-supervised learning
* Learning from imperfect data, Learning from mixed data
* Active learning, Ensemble learning

List of publications

*Google Scholar
*DBLP

Co-authors (a non-exhaustive list in alphabetical order)

*Yonatan Carlos Carranza Alarcón, Cassio de Campos, Sébastien Destercke, Johannes Fürnkranz, Xuan-Truong Hoang, Van-Nam Huynh, Eyke Hüllermeier, Mylène Masson, Eneldo Loza Mencía, Michael Rapp, Mohammad Hossein Shaker, Yang Yang, Haifei Zhang.

Few publications (illustrating types of research questions I have been tackling)

* Nguyen, V. L., Zhang, H., & Destercke, S. (2023). Learning Sets of Probabilities Through Ensemble Methods. In Proceedings of the 17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), pp. 1-14.

* 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.

* 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., & 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.

Post-doctorate researchers

Coming soon

* Kim-Dung Tran (co-supervised with Sébastien Destercke): Tensor decompositions and their applications in machine learning

In progress

* No

PhD students

In progress

* Thu-Ha Do (co-supervised with Yves Grandvalet): Probabilistic Graphical Models for Complex Learning Tasks

Defended

* No

Master thesis students

In progress

* Salvador Madrigal Castillo (co-supervised with Cyprien Gilet and Sébastien Destercke): “Minimax Classifiers for Multi-Label Classification”, University of Technology of Compiègne, France, 2024.

Defended

* Yang Yang (co-supervised with Cassio de Campos): Generalized Bayesian Network Classifiers, Eindhoven University of Technology, the Netherlands, 2022.

Other scientific activities

Conference Program committees

* AAAI (2021, 2023)
* AISTATS (2021-2022)
* UAI (2021)
* ECAI (2024)
* ISIPTA (2023)
* IUKM (2023)

Conference/workshop organization committees

* SMPS/BELIEF (2018)
* WUML & WPMSIIP (2017)

Reviewing activities

* International Journal of Approximate Reasoning (2023, 2024)
* Pattern Recognition (2023)
* ACM Transactions on Probabilistic Machine Learning (2024)

Grant reviewing

* National Science Center, Poland (2024)


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