3 papers
cs.LG2026
Causal Integrated Gradients: Scalable Feature Attribution Along Causal Paths
Joran Michiels, Anis Ismail, Maarten De Vos +1
Existing feature attribution methods that account for feature dependencies do not scale to high dimensions. We propose causal integrated gradients (CIG) and causal expected gradien…
cs.LG2023
Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions
Joran Michiels, Maarten De Vos, Johan Suykens
Model-agnostic feature attributions can provide local insights in complex ML models. If the explanation is correct, a domain expert can validate and trust the model's decision. How…
cs.LG2023
Explaining the Model and Feature Dependencies by Decomposition of the Shapley Value
Joran Michiels, Maarten De Vos, Johan Suykens
Shapley values have become one of the go-to methods to explain complex models to end-users. They provide a model agnostic post-hoc explanation with foundations in game theory: what…