4 citations · 20 across the 12 of their papers we have counts for
13 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…
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…
On the Change of Decision Boundaries and Loss in Learning with 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 may become i…