887 citations · 930 across the 11 of their papers we have counts for
14 papers
Aggregate Models, Not Explanations: Improving Feature Importance Estimation
Joseph Paillard, Angel Reyero Lobo, Denis A. Engemann +1
Feature-importance methods show promise in transforming machine learning models from predictive engines into tools for scientific discovery. However, due to data sampling and algor…
Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction
Joseph Paillard, Antoine Collas, Denis A. Engemann +1
Recent advances in machine learning have greatly expanded the repertoire of predictive methods for medical imaging. However, the interpretability of complex models remains a challe…
Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
Joseph Paillard, Angel Reyero Lobo, Vitaliy Kolodyazhniy +2
Causal machine learning holds promise for estimating individual treatment effects from complex data. For successful real-world applications of machine learning methods, it is of pa…
Geodesic Optimization for Predictive Shift Adaptation on EEG data
Apolline Mellot, Antoine Collas, Sylvain Chevallier +2
Electroencephalography (EEG) data is often collected from diverse contexts involving different populations and EEG devices. This variability can induce distribution shifts in the d…
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Apolline Mellot, Antoine Collas, Sylvain Chevallier +2
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require i…
Variable Importance in High-Dimensional Settings Requires Grouping
Ahmad Chamma, Bertrand Thirion, Denis A. Engemann
Explaining the decision process of machine learning algorithms is nowadays crucial for both model's performance enhancement and human comprehension. This can be achieved by assessi…