CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals. - Cédric Rommel; Thomas Moreau 001; Joseph Paillard; Alexandre Gramfort (ICLR, 2022)

Data augmentation for learning predictive models on EEG: a systematic comparison. - Cédric Rommel; Joseph Paillard; Thomas Moreau 001; Alexandre Gramfort (CoRR, 2022)

Measuring Variable Importance in Individual Treatment Effect Estimation with High Dimensional Data. - Joseph Paillard; Vitaliy Kolodyazhniy; Bertrand Thirion; Denis A. Engemann (CoRR, 2024)

Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction. - Joseph Paillard; Antoine Collas; Denis A. Engemann; Bertrand Thirion (IPMI (2), 2025)

GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals. - Joseph Paillard; Jörg F. Hipp; Denis A. Engemann (Patterns, 2025)