The Shapley Additive Global Importance (SAGE) value is a theoretically
a...
Algorithmic recourse recommendations, such as Karimi et al.'s (2021) cau...
Algorithmic recourse explanations inform stakeholders on how to act to r...
Global model-agnostic feature importance measures either quantify whethe...
We consider the problem of causal structure learning in the setting of
h...
Interpretable Machine Learning (IML) methods are used to gain insight in...
Modern requirements for machine learning (ML) models include both high
p...
Neurophysiological studies are typically conducted in laboratories with
...
We consider the task of causal structure learning over measurement depen...
Methods based on Riemannian geometry have proven themselves to be good m...
Complex systems can be modelled at various levels of detail. Ideally, ca...
This workshop explores the interface between cognitive neuroscience and
...
While the channel capacity reflects a theoretical upper bound on the
ach...
Causal inference concerns the identification of cause-effect relationshi...
Pattern recognition in neuroimaging distinguishes between two types of
m...
While invasively recorded brain activity is known to provide detailed
in...
Causal inference concerns the identification of cause-effect relationshi...
Causal terminology is often introduced in the interpretation of encoding...