19 citations · 34 across the 22 of their papers we have counts for
3 papers · 1 filter
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…