2 papers
cs.LG2026
Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
Matheus Camilo da Silva, Leonardo Arrighi, Ana Carolina Lorena +1
AutoClustering methods aim to automate unsupervised learning tasks, including algorithm selection (AS), hyperparameter optimization (HPO), and pipeline synthesis (PS), by often lev…
cs.LG2025
Filtering instances and rejecting predictions to obtain reliable models in healthcare
Maria Gabriela Valeriano, David Kohan Marzagão, Alfredo Montelongo +3
Machine Learning (ML) models are widely used in high-stakes domains such as healthcare, where the reliability of predictions is critical. However, these models often fail to accoun…