Using Conditional Copula to Estimate Value-at-Risk in Vietnam's Foreign Exchange Market. - Vu-Linh Nguyen; Van-Nam Huynh (Econometrics of Risk, 2015)

Querying Partially Labelled Data to Improve a K-nn Classifier. - Vu-Linh Nguyen; Sébastien Destercke; Marie-Hélène Masson (AAAI, 2017)

K-Nearest Neighbour Classification for Interval-Valued Data. - Vu-Linh Nguyen; Sébastien Destercke; Marie-Hélène Masson (SUM, 2017)

Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. - Vu-Linh Nguyen; Sébastien Destercke; Marie-Hélène Masson; Eyke Hüllermeier (IJCAI, 2018)

Partial data querying through racing algorithms. - Vu-Linh Nguyen; Sébastien Destercke; Marie-Hélène Masson (Int. J. Approx. Reason., 2018)

Imprecision in machine learning problems. (Imprécision en apprentissage statistique). - Vu-Linh Nguyen (2018)

Epistemic Uncertainty Sampling. - Vu-Linh Nguyen; Sébastien Destercke; Eyke Hüllermeier (DS, 2019)

Reliable Multi-label Classification: Prediction with Partial Abstention. - Vu-Linh Nguyen; Eyke Hüllermeier (CoRR, 2019)

Reliable Multilabel Classification: Prediction with Partial Abstention. - Vu-Linh Nguyen; Eyke Hüllermeier (AAAI, 2020)

On Aggregation in Ensembles of Multilabel Classifiers. - Vu-Linh Nguyen; Eyke Hüllermeier; Michael Rapp; Eneldo Loza Mencía; Johannes Fürnkranz (DS, 2020)

Learning Gradient Boosted Multi-label Classification Rules. - Michael Rapp; Eneldo Loza Mencía; Johannes Fürnkranz; Vu-Linh Nguyen; Eyke Hüllermeier (ECML/PKDD (3), 2020)

Rule-Based Multi-label Classification: Challenges and Opportunities. - Eyke Hüllermeier; Johannes Fürnkranz; Eneldo Loza Mencía; Vu-Linh Nguyen; Michael Rapp (RuleML+RR, 2020)

Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence. - Vu-Linh Nguyen; Eyke Hüllermeier (J. Artif. Intell. Res., 2021)

Racing trees to query partial data. - Vu-Linh Nguyen; Sébastien Destercke; Marie-Hélène Masson; Rashad Ghassani (Soft Comput., 2021)

Skeptical inferences in multi-label ranking with sets of probabilities. - Yonatan Carlos Carranza Alarcón; Vu-Linh Nguyen (CoRR, 2022)

How to measure uncertainty in uncertainty sampling for active learning. - Vu-Linh Nguyen; Mohammad Hossein Shaker; Eyke Hüllermeier (Mach. Learn., 2022)

Learning Sets of Probabilities Through Ensemble Methods. - Vu-Linh Nguyen; Haifei Zhang; Sébastien Destercke (ECSQARU, 2023)

Inference Problem in Probabilistic Multi-label Classification. - Vu-Linh Nguyen; Xuan-Truong Hoang; Van-Nam Huynh (IUKM (2), 2023)

Probabilistic Multi-Dimensional Classification. - Vu-Linh Nguyen; Yang Yang; Cassio de Campos (UAI, 2023)

Towards A Wearable Passive Lower-Limb Exoskeleton Based on the Compact Gear-Spring Mechanism. - Quang Phuoc Minh Pham; Hung Quang Nguyen; Quan Minh Vo; Tien Dac Lai; Hien Vu Thu Nguyen; Vu-Linh Nguyen (ROBIO, 2024)

Discrete Minimax Binary Relevance Classifier for Imbalanced Multi-label Classification. - Salvador Madrigal; Vu-Linh Nguyen; Cyprien Gilet; Sébastien Destercke (SUM, 2024)

Accelerate K-Mode Algorithms Using The Triangle Inequality. - Vu-Linh Nguyen; Toan Nguyen Mau; Van-Nam Huynh (SUM, 2024)

Self-explaining Neural Network for Multi-criteria Sentiment Analysis. - Xuan-Truong Hoang; Vu-Linh Nguyen; Takayuki Kitagawa; Van-Nam Huynh (IUKM (1), 2025)

Robust Classification in Bayesian Neural Networks. - Kim-Dung Tran; Xuan-Truong Hoang; Vu-Linh Nguyen; Sébastien Destercke; Van-Nam Huynh (IUKM (1), 2025)

Credal ensembling in multi-class classification. - Vu-Linh Nguyen; Haifei Zhang; Sébastien Destercke (Mach. Learn., 2025)

Robust Explanations Through Uncertainty Decomposition: A Path to Trustworthier AI. - Chenrui Zhu; Louenas Bounia; Vu-Linh Nguyen; Sébastien Destercke; Arthur Hoarau (CoRR, 2025)