53 citations · 173 across the 27 of their papers we have counts for
7 papers · 1 filter
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
Maximilian Muschalik, Fabian Fumagalli, Rohit Jagtani +2
Post-hoc explanation techniques such as the well-established partial dependence plot (PDP), which investigates feature dependencies, are used in explainable artificial intelligence…
For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAI
Ulrike Kuhl, André Artelt, Barbara Hammer
Counterfactual explanations (CFEs) are a popular approach in explainable artificial intelligence (xAI), highlighting changes to input data necessary for altering a model's output.…
Adversarial Attacks on Leakage Detectors in Water Distribution Networks
Paul Stahlhofen, André Artelt, Luca Hermes +1
Many Machine Learning models are vulnerable to adversarial attacks: There exist methodologies that add a small (imperceptible) perturbation to an input such that the model comes up…
Model Based Explanations of Concept Drift
Fabian Hinder, Valerie Vaquet, Johannes Brinkrolf +1
The notion of concept drift refers to the phenomenon that the distribution generating the observed data changes over time. If drift is present, machine learning models can become i…
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams
Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer +1
Existing methods for explainable artificial intelligence (XAI), including popular feature importance measures such as SAGE, are mostly restricted to the batch learning scenario. Ho…
SHAP-IQ: Unified Approximation of any-order Shapley Interactions
Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki +2
Predominately in explainable artificial intelligence (XAI) research, the Shapley value (SV) is applied to determine feature attributions for any black box model. Shapley interactio…
Combining self-labeling and demand based active learning for non-stationary data streams
Valerie Vaquet, Fabian Hinder, Johannes Brinkrolf +1
Learning from non-stationary data streams is a research direction that gains increasing interest as more data in form of streams becomes available, for example from social media, s…