| 2025 | AISTATS | A primer on linear classification with missing data. | Angel David Reyero Lobo, Alexis Ayme, Claire Boyer, Erwan Scornet |
| 2025 | ICML | Quantifying Treatment Effects: Estimating Risk Ratios via Observational Studies. | Ahmed Boughdiri, Julie Josse, Erwan Scornet |
| 2024 | ICML | Random features models: a way to study the success of naive imputation. | Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet |
| 2023 | AISTATS | Is interpolation benign for random forest regression? | Ludovic Arnould, Claire Boyer, Erwan Scornet |
| 2023 | ICLR | Sparse tree-based Initialization for Neural Networks. | Patrick Lutz, Ludovic Arnould, Claire Boyer, Erwan Scornet |
| 2023 | ICML | Naive imputation implicitly regularizes high-dimensional linear models. | Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet |
| 2022 | AISTATS | SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. | Clment Bnard, Grard Biau, Sbastien Da Veiga, Erwan Scornet |
| 2022 | ICML | Near-optimal rate of consistency for linear models with missing values. | Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet |
| 2021 | AISTATS | Interpretable Random Forests via Rule Extraction. | Clment Bnard, Grard Biau, Sbastien Da Veiga, Erwan Scornet |
| 2021 | ICML | Analyzing the tree-layer structure of Deep Forests. | Ludovic Arnould, Claire Boyer, Erwan Scornet |
| 2020 | AISTATS | Linear predictor on linearly-generated data with missing values: non consistency and solutions. | Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gal Varoquaux |