5 citations · 9 across the 6 of their papers we have counts for
6 papers
shapiq: Shapley Interactions for Machine Learning
Maximilian Muschalik, Hubert Baniecki, Fabian Fumagalli +3
Originally rooted in game theory, the Shapley Value (SV) has recently become an important tool in machine learning research. Perhaps most notably, it is used for feature attributio…
Red-Teaming Segment Anything Model
Krzysztof Jankowski, Bartlomiej Sobieski, Mateusz Kwiatkowski +4
Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications. The Segment…
Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI
Vladimir Zaigrajew, Hubert Baniecki, Lukasz Tulczyjew +4
Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for ide…
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…
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…
Do not explain without context: addressing the blind spot of model explanations
Katarzyna Woźnica, Katarzyna Pękala, Hubert Baniecki +3
The increasing number of regulations and expectations of predictive machine learning models, such as so called right to explanation, has led to a large number of methods promising…