5 citations · 5 across the 3 of their papers we have counts for
3 papers · 1 filter
Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks
Maximilian Muschalik, Fabian Fumagalli, Paolo Frazzetto +5
Albeit the ubiquitous use of Graph Neural Networks (GNNs) in machine learning (ML) prediction tasks involving graph-structured data, their interpretability remains challenging. In…
Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory
Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier +2
Feature-based explanations, using perturbations or gradients, are a prevalent tool to understand decisions of black box machine learning models. Yet, differences between these meth…
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…