publications

2026

1. 

   Aggregate Models, Not Explanations: Improving Feature Importance Estimation

   Joseph Paillard, Angel Reyero Lobo, Denis A Engemann, and Bertrand Thirion

   International Conference on Machine Learning (ICML), 2026

   Bib PDF Code

   @article{paillard2026aggregate,
     title = {Aggregate Models, Not Explanations: Improving Feature Importance Estimation},
     author = {Paillard, Joseph and Lobo, Angel Reyero and Engemann, Denis A and Thirion, Bertrand},
     journal = {International Conference on Machine Learning (ICML)},
     year = {2026},
   }

2025

1. 

   Measuring variable importance in heterogeneous treatment effects with confidence

   Joseph Paillard, Angel Reyero Lobo, Vitaliy Kolodyazhniy, Bertrand Thirion, and Denis A Engemann

   International Conference on Machine Learning (ICML), 2025

   Bib PDF Code

   @article{paillard2025measuring,
     title = {Measuring variable importance in heterogeneous treatment effects with confidence},
     author = {Paillard, Joseph and Lobo, Angel Reyero and Kolodyazhniy, Vitaliy and Thirion, Bertrand and Engemann, Denis A},
     journal = {International Conference on Machine Learning (ICML)},
     year = {2025},
   }

2. 

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

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

   In International Conference on Information Processing in Medical Imaging, 2025

   Bib PDF Code

   @inproceedings{paillard2025hierarchical,
     title = {Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction},
     author = {Paillard, Joseph and Collas, Antoine and Engemann, Denis A and Thirion, Bertrand},
     booktitle = {International Conference on Information Processing in Medical Imaging},
     pages = {79--93},
     year = {2025},
     organization = {Springer},
   }

3. 

   GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals

   Joseph Paillard, Jörg F Hipp, and Denis A Engemann

   Patterns, 2025

   Bib PDF Code

   @article{paillard2025green,
     title = {GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals},
     author = {Paillard, Joseph and Hipp, J{\"o}rg F and Engemann, Denis A},
     journal = {Patterns},
     volume = {6},
     number = {3},
     year = {2025},
     publisher = {Elsevier},
   }

4. 

   Benchmarking the utility of dry-electrode electroencephalography for clinical trials

   Joseph Paillard^*, Philipp Bomatter^*, Laura Dubreuil-Vall, Jörg Felix Hipp, and David Johannes Hawellek

   Scientific Reports, 2025

   Bib PDF

   @article{paillard2025benchmarking,
     title = {Benchmarking the utility of dry-electrode electroencephalography for clinical trials},
     author = {Paillard, Joseph and Bomatter, Philipp and Dubreuil-Vall, Laura and Hipp, J{\"o}rg Felix and Hawellek, David Johannes},
     journal = {Scientific Reports},
     volume = {15},
     number = {1},
     pages = {33667},
     year = {2025},
     publisher = {Nature Publishing Group UK London},
   }

2024

1. 

   Machine learning of brain-specific biomarkers from EEG

   Philipp Bomatter, Joseph Paillard, Pilar Garces, Jörg Hipp, and Denis-Alexander Engemann

   EBioMedicine, 2024

   PDF Code

2022

1. 

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

   Cédric Rommel, Joseph Paillard, Thomas Moreau, and Alexandre Gramfort

   Journal of Neural Engineering, 2022

   Bib PDF Code

   @article{rommel2022data,
     title = {Data augmentation for learning predictive models on EEG: a systematic comparison},
     author = {Rommel, C{\'e}dric and Paillard, Joseph and Moreau, Thomas and Gramfort, Alexandre},
     journal = {Journal of Neural Engineering},
     volume = {19},
     number = {6},
     year = {2022},
     publisher = {IOP Publishing},
   }

2. 

   CADDA: Class-wise automatic differentiable data augmentation for EEG signals

   Cédric Rommel, Thomas Moreau, Joseph Paillard, and Alexandre Gramfort

   International Conference on Learning Representations (ICLR), 2022

   Bib PDF

   @article{rommel2021cadda,
     title = {CADDA: Class-wise automatic differentiable data augmentation for EEG signals},
     author = {Rommel, C{\'e}dric and Moreau, Thomas and Paillard, Joseph and Gramfort, Alexandre},
     journal = {International Conference on Learning Representations (ICLR)},
     year = {2022},
   }