| A random forest guided tour G Biau, E Scornet Test 25 (2), 197-227, 2016 | 4110 | 2016 |
| Consistency of random forests E Scornet, G Biau, JP Vert | 743 | 2015 |
| Random forests and kernel methods E Scornet IEEE Transactions on Information Theory 62 (3), 1485-1500, 2016 | 316 | 2016 |
| Tuning parameters in random forests E Scornet ESAIM: Proceedings and surveys 60, 144-162, 2017 | 198 | 2017 |
| On the consistency of supervised learning with missing values J Josse, JM Chen, N Prost, G Varoquaux, E Scornet Statistical Papers 65 (9), 5447-5479, 2024 | 144 | 2024 |
| Neural random forests G Biau, E Scornet, J Welbl Sankhya A 81 (2), 347-386, 2019 | 143 | 2019 |
| Trees, forests, and impurity-based variable importance in regression E Scornet Annales de l'Institut Henri Poincare (B) Probabilites et statistiques 59 (1 …, 2023 | 130 | 2023 |
| On the asymptotics of random forests E Scornet Journal of Multivariate Analysis 146, 72-83, 2016 | 125 | 2016 |
| Prediction of human population responses to toxic compounds by a collaborative competition F Eduati, LM Mangravite, T Wang, H Tang, JC Bare, R Huang, T Norman, ... Nature biotechnology 33 (9), 933-940, 2015 | 120 | 2015 |
| Interpretable random forests via rule extraction C Bénard, G Biau, S Da Veiga, E Scornet International conference on artificial intelligence and statistics, 937-945, 2021 | 92 | 2021 |
| Mean decrease accuracy for random forests: inconsistency, and a practical solution via the Sobol-MDA C Bénard, S Da Veiga, E Scornet Biometrika 109 (4), 881-900, 2022 | 86 | 2022 |
| What’sa good imputation to predict with missing values? M Le Morvan, J Josse, E Scornet, G Varoquaux Advances in Neural Information Processing Systems 34, 11530-11540, 2021 | 79 | 2021 |
| Sirus: Stable and interpretable rule set for classification C Bénard, G Biau, S Da Veiga, E Scornet | 63 | 2021 |
| NeuMiss networks: differentiable programming for supervised learning with missing values. M Le Morvan, J Josse, T Moreau, E Scornet, G Varoquaux Advances in Neural Information Processing Systems 33, 5980-5990, 2020 | 59 | 2020 |
| Minimax optimal rates for Mondrian trees and forests J Mourtada, S Gaïffas, E Scornet | 50 | 2020 |
| Impact of subsampling and tree depth on random forests R Duroux, E Scornet ESAIM: Probability and Statistics 22, 96-128, 2018 | 48 | 2018 |
| SHAFF: Fast and consistent SHApley eFfect estimates via random Forests C Bénard, G Biau, S Da Veiga, E Scornet International Conference on Artificial Intelligence and Statistics, 5563-5582, 2022 | 38 | 2022 |
| Linear predictor on linearly-generated data with missing values: non consistency and solutions M Le Morvan, N Prost, J Josse, E Scornet, G Varoquaux International Conference on Artificial Intelligence and Statistics, 3165-3174, 2020 | 37 | 2020 |
| Rejoinder on: A random forest guided tour G Biau, E Scornet Test 25, 264-268, 2016 | 35 | 2016 |
| AMF: Aggregated Mondrian forests for online learning J Mourtada, S Gaïffas, E Scornet Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2021 | 33 | 2021 |
