3 papers
cs.LG2025
Explainability of Machine Learning Models under Missing Data
Tuan L. Vo, Thu Nguyen, Luis M. Lopez-Ramos +3
Missing data is a prevalent issue that can significantly impair model performance and explainability. This paper briefly summarizes the development of the field of missing data wit…
stat.ML2025
DPERC: Direct Parameter Estimation for Mixed Data
Tuan L. Vo, Quan Huu Do, Uyen Dang +4
The covariance matrix is a foundation in numerous statistical and machine-learning applications such as Principle Component Analysis, Correlation Heatmap, etc. However, missing val…
cs.LG2024
Directly Handling Missing Data in Linear Discriminant Analysis for Enhancing Classification Accuracy and Interpretability
Tuan L. Vo, Uyen Dang, Thu Nguyen
As the adoption of Artificial Intelligence (AI) models expands into critical real-world applications, ensuring the explainability of these models becomes paramount, particularly in…