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04/06/2023
Efficient SAGE Estimation via Causal Structure Learning
The Shapley Additive Global Importance (SAGE) value is a theoretically a...
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10/27/2022
Improvement-Focused Causal Recourse (ICR)
Algorithmic recourse recommendations, such as Karimi et al.'s (2021) cau...
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07/16/2021
A Causal Perspective on Meaningful and Robust Algorithmic Recourse
Algorithmic recourse explanations inform stakeholders on how to act to r...
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06/15/2021
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)
Global model-agnostic feature importance measures either quantify whethe...
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06/07/2021
A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations
We consider the problem of causal structure learning in the setting of h...
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07/16/2020
Relative Feature Importance
Interpretable Machine Learning (IML) methods are used to gain insight in...
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07/08/2020
Pitfalls to Avoid when Interpreting Machine Learning Models
Modern requirements for machine learning (ML) models include both high p...
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02/26/2020
MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware
Neurophysiological studies are typically conducted in laboratories with ...
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10/19/2019
Measurement Dependence Inducing Latent Causal Models
We consider the task of causal structure learning over measurement depen...
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09/23/2019
Tangent space spatial filters for interpretable and efficient Riemannian classification
Methods based on Riemannian geometry have proven themselves to be good m...
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07/04/2017
Causal Consistency of Structural Equation Models
Complex systems can be modelled at various levels of detail. Ideally, ca...
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01/06/2017
NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)
This workshop explores the interface between cognitive neuroscience and ...
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05/23/2016
A note on the expected minimum error probability in equientropic channels
While the channel capacity reflects a theoretical upper bound on the ach...
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05/02/2016
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data
Causal inference concerns the identification of cause-effect relationshi...
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12/15/2015
Causal and anti-causal learning in pattern recognition for neuroimaging
Pattern recognition in neuroimaging distinguishes between two types of m...
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12/14/2015
Decoding index finger position from EEG using random forests
While invasively recorded brain activity is known to provide detailed in...
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12/03/2015
MERLiN: Mixture Effect Recovery in Linear Networks
Causal inference concerns the identification of cause-effect relationshi...
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11/15/2015
