3 papers
cs.LG2025
Explaining Concept Drift through the Evolution of Group Counterfactuals
Ignacy Stępka, Jerzy Stefanowski
Machine learning models in dynamic environments often suffer from concept drift, where changes in the data distribution degrade performance. While detecting this drift is a well-st…
cs.CV2025
DetoxAI: a Python Toolkit for Debiasing Deep Learning Models in Computer Vision
Ignacy Stępka, Lukasz Sztukiewicz, Michał Wiliński +1
While machine learning fairness has made significant progress in recent years, most existing solutions focus on tabular data and are poorly suited for vision-based classification t…
cs.LG2024
Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
Ignacy Stępka, Mateusz Lango, Jerzy Stefanowski
Counterfactual explanations (CFEs) guide users on how to adjust inputs to machine learning models to achieve desired outputs. While existing research primarily addresses static sce…