8 citations · 10 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers
Ignacy Stępka, Mateusz Lango, Jerzy Stefanowski
Counterfactuals are widely used to explain ML model predictions by providing alternative scenarios for obtaining the more desired predictions. They can be generated by a variety of…
Deep Similarity Learning Loss Functions in Data Transformation for Class Imbalance
Damian Horna, Lango Mateusz, Jerzy Stefanowski
Improving the classification of multi-class imbalanced data is more difficult than its two-class counterpart. In this paper, we use deep neural networks to train new representation…
The Problem of Coherence in Natural Language Explanations of Recommendations
Jakub Raczyński, Mateusz Lango, Jerzy Stefanowski
Providing natural language explanations for recommendations is particularly useful from the perspective of a non-expert user. Although several methods for providing such explanatio…