3 papers
cs.SE2026
Are We Lost in the Woods? Detecting Silent Semantic Faults for Random Forest Classifiers with Data-informed Static Analysis
Willem Meijer, Louis Ohl, Kristian Sandahl +1
While machine learning (ML) software necessitates effective quality assurance, ML engineers still encounter silent semantic faults, such as imbalanced datasets, that degrade predic…
stat.ML2025
A Tutorial on Discriminative Clustering and Mutual Information
Louis Ohl, Pierre-Alexandre Mattei, Frédéric Precioso
To cluster data is to separate samples into distinctive groups that should ideally have some cohesive properties. Today, numerous clustering algorithms exist, and their differences…
cs.LG2025
Discriminative Ordering Through Ensemble Consensus
Louis Ohl, Fredrik Lindsten
Evaluating the performance of clustering models is a challenging task where the outcome depends on the definition of what constitutes a cluster. Due to this design, current existin…