Erwan Scornet
Erwan Scornet
Maître de conférence, Ecole Polytechnique
Verified email at polytechnique.edu
Title
Cited by
Cited by
Year
A random forest guided tour
G Biau, E Scornet
Test 25 (2), 197-227, 2016
6482016
Consistency of random forests
E Scornet, G Biau, JP Vert
The Annals of Statistics 43 (4), 1716-1741, 2015
3312015
Random forests and kernel methods
E Scornet
IEEE Transactions on Information Theory 62 (3), 1485-1500, 2016
1142016
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
942015
On the asymptotics of random forests
E Scornet
Journal of Multivariate Analysis 146, 72-83, 2016
722016
Neural random forests
G Biau, E Scornet, J Welbl
Sankhya A 81 (2), 347-386, 2019
632019
Tuning parameters in random forests
E Scornet
ESAIM: Proceedings and Surveys 60, 144-162, 2017
512017
On the consistency of supervised learning with missing values
J Josse, N Prost, E Scornet, G Varoquaux
arXiv preprint arXiv:1902.06931, 2019
332019
Impact of subsampling and pruning on random forests
R Duroux, E Scornet
arXiv preprint arXiv:1603.04261, 2016
152016
Universal consistency and minimax rates for online mondrian forests
J Mourtada, S Gaïffas, E Scornet
arXiv preprint arXiv:1711.02887, 2017
142017
Impact of subsampling and tree depth on random forests
R Duroux, E Scornet
ESAIM: Probability and Statistics 22, 96-128, 2018
132018
Minimax optimal rates for mondrian trees and forests
J Mourtada, S Gaïffas, E Scornet
arXiv preprint arXiv:1803.05784, 2018
122018
Interpretable random forests via rule extraction
C Bénard, G Biau, S Veiga, E Scornet
International Conference on Artificial Intelligence and Statistics, 937-945, 2021
92021
Rejoinder on: A random forest guided tour
G Biau, E Scornet
Test 25 (2), 264-268, 2016
82016
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
72020
SIRUS: making random forests interpretable
C Bénard, G Biau, S Da Veiga, E Scornet
arXiv preprint arXiv:1908.06852, 2019
72019
Sirus: Stable and interpretable rule set for classification
C Bénard, G Biau, S Da Veiga, E Scornet
Electronic Journal of Statistics 15 (1), 427-505, 2021
52021
Trees, forests, and impurity-based variable importance
E Scornet
arXiv preprint arXiv:2001.04295, 2020
52020
Apprentissage et forêts aléatoires
E Scornet
Paris 6, 2015
52015
Kernel multitask regression for toxicogenetics
E Bernard, Y Jiao, E Scornet, V Stoven, T Walter, JP Vert
Molecular informatics 36 (10), 1700053, 2017
42017
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