| 2026 | ICPR | Robust Explanations Through Uncertainty Decomposition: A Path to Trustworthier AI. | Chenrui Zhu, Louenas Bounia, Vu-Linh Nguyen, Sbastien Destercke, Arthur Hoarau |
| 2025 | ECSQARU | Discrete Minimax Probabilistic Classifier Chains for Multi-label Classification Under Label Imbalance. | Salvador Madrigal, Cyprien Gilet, Vu-Linh Nguyen, Sbastien Destercke |
| 2025 | ECSQARU | Robust Explanations: The Case of Prime Implicants. | Chenrui Zhu, Vu-Linh Nguyen, Marie-Hlne Masson, Sbastien Destercke |
| 2023 | ECSQARU | Learning Sets of Probabilities Through Ensemble Methods. | Vu-Linh Nguyen, Haifei Zhang, Sbastien Destercke |
| 2023 | UAI | Probabilistic Multi-Dimensional Classification. | Vu-Linh Nguyen, Yang Yang, Cassio de Campos |
| 2020 | AAAI | Reliable Multilabel Classification: Prediction with Partial Abstention. | Vu-Linh Nguyen, Eyke Hllermeier |
| 2020 | DIS | On Aggregation in Ensembles of Multilabel Classifiers. | Vu-Linh Nguyen, Eyke Hllermeier, Michael Rapp, Eneldo Loza Menca, Johannes Frnkranz |
| 2019 | DIS | Epistemic Uncertainty Sampling. | Vu-Linh Nguyen, Sbastien Destercke, Eyke Hllermeier |
| 2018 | IJCAI | Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. | Vu-Linh Nguyen, Sbastien Destercke, Marie-Hlne Masson, Eyke Hllermeier |
| 2017 | AAAI | Querying Partially Labelled Data to Improve a K-nn Classifier. | Vu-Linh Nguyen, Sbastien Destercke, Marie-Hlne Masson |