Joseph Paillard

According to our database, Joseph Paillard authored at least 8 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Aggregate Models, Not Explanations: Improving Feature Importance Estimation.

Joseph Paillard, Angel David Reyero Lobo, Denis A. Engemann, Bertrand Thirion

CoRR, February, 2026

2025

Braindecode: toolbox for decoding raw electrophysiological brain data with deep learning models.

Bruno Aristimunha, Pierre Guetshel, Martin Wimpff, Lukas Gemein, Cédric Rommel, Hubert J. Banville, Maciej Sliwowski, Daniel Wilson, Simon Brandt, Théo Gnassounou, Joseph Paillard, Bruna Lopes, Sara Sedlar, Thomas Moreau, Sylvain Chevallier, Alexandre Gramfort, Robin Tibor Schirrmeister

Dataset, December, 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

Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction.

Joseph Paillard, Antoine Collas, Denis A. Engemann, Bertrand Thirion

Proceedings of the Information Processing in Medical Imaging, 2025

Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence.

Joseph Paillard, Angel David Reyero Lobo, Vitaliy Kolodyazhniy, Bertrand Thirion, Denis-Alexander Engemann

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Measuring Variable Importance in Individual Treatment Effect Estimation with High Dimensional Data.

Joseph Paillard, Vitaliy Kolodyazhniy, Bertrand Thirion, Denis A. Engemann

CoRR, 2024

2022

Data augmentation for learning predictive models on EEG: a systematic comparison.

Cédric Rommel, Joseph Paillard, Thomas Moreau, Alexandre Gramfort

CoRR, 2022

CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals.

Cédric Rommel, Thomas Moreau, Joseph Paillard, Alexandre Gramfort

Proceedings of the Tenth International Conference on Learning Representations, 2022