167 citations · 557 across the 21 of their papers we have counts for
7 papers · 1 filter
Be Careful When Evaluating Explanations Regarding Ground Truth
Hubert Baniecki, Maciej Chrabaszcz, Andreas Holzinger +3
Evaluating explanations of image classifiers regarding ground truth, e.g. segmentation masks defined by human perception, primarily evaluates the quality of the models under consid…
survex: an R package for explaining machine learning survival models
Mikołaj Spytek, Mateusz Krzyziński, Sophie Hanna Langbein +3
Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread a…
Explaining and visualizing black-box models through counterfactual paths
Bastian Pfeifer, Mateusz Krzyzinski, Hubert Baniecki +3
Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable. In this paper, we propose a…
Adversarial attacks and defenses in explainable artificial intelligence: A survey
Hubert Baniecki, Przemyslaw Biecek
Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging and trusting statistical and deep learning models, as well as interpreting their predictio…
Towards Evaluating Explanations of Vision Transformers for Medical Imaging
Piotr Komorowski, Hubert Baniecki, Przemysław Biecek
As deep learning models increasingly find applications in critical domains such as medical imaging, the need for transparent and trustworthy decision-making becomes paramount. Many…
Interpretable machine learning for time-to-event prediction in medicine and healthcare
Hubert Baniecki, Bartlomiej Sobieski, Patryk Szatkowski +2
Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications. However, onl…
Performance is not enough: the story told by a Rashomon quartet
Przemyslaw Biecek, Hubert Baniecki, Mateusz Krzyzinski +1
The usual goal of supervised learning is to find the best model, the one that optimizes a particular performance measure. However, what if the explanation provided by this model is…