3 papers
cs.LG2026
GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering
Christos Fragkathoulas, Eleni Psaroudaki, Themis Palpanas +1
Time-series clustering is a fundamental tool for pattern discovery, yet existing explainability methods, primarily based on feature attribution or metadata, fail to identify the tr…
cs.LG2025
FACEGroup: Feasible and Actionable Counterfactual Explanations for Group Fairness
Christos Fragkathoulas, Vasiliki Papanikou, Evaggelia Pitoura +1
Counterfactual explanations assess unfairness by revealing how inputs must change to achieve a desired outcome. This paper introduces the first graph-based framework for generating…
cs.LG2025
UGCE: User-Guided Incremental Counterfactual Exploration
Christos Fragkathoulas, Evaggelia Pitoura
Counterfactual explanations (CFEs) are a popular approach for interpreting machine learning predictions by identifying minimal feature changes that alter model outputs. However, in…