11 citations · 14 across the 3 of their papers we have counts for
5 papers · 1 filter
A Causal Perspective on Meaningful and Robust Algorithmic Recourse
Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup
Algorithmic recourse explanations inform stakeholders on how to act to revert unfavorable predictions. However, in general ML models do not predict well in interventional distribut…
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)
Gunnar König, Timo Freiesleben, Bernd Bischl +2
Global model-agnostic feature importance measures either quantify whether features are directly used for a model's predictions (direct importance) or whether they contain predictio…
Relative Feature Importance
Gunnar König, Christoph Molnar, Bernd Bischl +1
Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model. Commonly used IML methods differ i…
Measurement Dependence Inducing Latent Causal Models
Alex Markham, Moritz Grosse-Wentrup
We consider the task of causal structure learning over measurement dependence inducing latent (MeDIL) causal models. We show that this task can be framed in terms of the graph theo…
Proceedings of the 5th Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) at NIPS 2015
I. Rish, L. Wehbe, G. Langs +3
This volume is a collection of contributions from the 5th Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) at the Neural Information Processing Systems (NIPS…