4 citations · 4 across the 4 of their papers we have counts for
4 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…
Data augmentation for learning predictive models on EEG: a systematic comparison
Cédric Rommel, Joseph Paillard, Thomas Moreau +1
Objective: The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the re…