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Erwan Scornet
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2020 – today
- 2024
- [c12]Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet:
Random features models: a way to study the success of naive imputation. ICML 2024 - [i14]Abdoulaye Sakho, Erwan Scornet, Emmanuel Malherbe:
Theoretical and experimental study of SMOTE: limitations and comparisons of rebalancing strategies. CoRR abs/2402.03819 (2024) - 2023
- [c11]Ludovic Arnould, Claire Boyer, Erwan Scornet:
Is interpolation benign for random forest regression? AISTATS 2023: 5493-5548 - [c10]Patrick Lutz, Ludovic Arnould, Claire Boyer, Erwan Scornet:
Sparse tree-based Initialization for Neural Networks. ICLR 2023 - [c9]Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet:
Naive imputation implicitly regularizes high-dimensional linear models. ICML 2023: 1320-1340 - 2022
- [c8]Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. AISTATS 2022: 5563-5582 - [c7]Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet:
Near-optimal rate of consistency for linear models with missing values. ICML 2022: 1211-1243 - [i13]Alexis Ayme, Claire Boyer, Aymeric Dieuleveut
, Erwan Scornet:
Minimax rate of consistency for linear models with missing values. CoRR abs/2202.01463 (2022) - [i12]Patrick Lutz, Ludovic Arnould, Claire Boyer, Erwan Scornet:
Sparse tree-based initialization for neural networks. CoRR abs/2209.15283 (2022) - 2021
- [c6]Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
Interpretable Random Forests via Rule Extraction. AISTATS 2021: 937-945 - [c5]Ludovic Arnould, Claire Boyer, Erwan Scornet:
Analyzing the tree-layer structure of Deep Forests. ICML 2021: 342-350 - [c4]Marine Le Morvan, Julie Josse, Erwan Scornet, Gaël Varoquaux:
What's a good imputation to predict with missing values? NeurIPS 2021: 11530-11540 - [i11]Clément Bénard, Sébastien Da Veiga, Erwan Scornet:
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA. CoRR abs/2102.13347 (2021) - [i10]Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. CoRR abs/2105.11724 (2021) - [i9]Marine Le Morvan, Julie Josse, Erwan Scornet, Gaël Varoquaux:
What's a good imputation to predict with missing values? CoRR abs/2106.00311 (2021) - 2020
- [c3]Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gaël Varoquaux:
Linear predictor on linearly-generated data with missing values: non consistency and solutions. AISTATS 2020: 3165-3174 - [c2]Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux:
NeuMiss networks: differentiable programming for supervised learning with missing values. NeurIPS 2020 - [i8]Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gaël Varoquaux:
Linear predictor on linearly-generated data with missing values: non consistency and solutions. CoRR abs/2002.00658 (2020) - [i7]Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
Interpretable Random Forests via Rule Extraction. CoRR abs/2004.14841 (2020) - [i6]Marine Le Morvan, Julie Josse, Thomas Moreau
Neumann networks: differential programming for supervised learning with missing values. CoRR abs/2007.01627 (2020) - [i5]Ludovic Arnould, Claire Boyer, Erwan Scornet:
Analyzing the tree-layer structure of Deep Forests. CoRR abs/2010.15690 (2020)
2010 – 2019
- 2019
- [i4]Julie Josse, Nicolas Prost, Erwan Scornet, Gaël Varoquaux:
On the consistency of supervised learning with missing values. CoRR abs/1902.06931 (2019) - [i3]Jaouad Mourtada
AMF: Aggregated Mondrian Forests for Online Learning. CoRR abs/1906.10529 (2019) - [i2]Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
SIRUS: making random forests interpretable. CoRR abs/1908.06852 (2019) - 2017
- [c1]Jaouad Mourtada, Stéphane Gaïffas, Erwan Scornet:
Universal consistency and minimax rates for online Mondrian Forests. NIPS 2017: 3758-3767 - 2016
- [j2]Erwan Scornet:
On the asymptotics of random forests. J. Multivar. Anal. 146: 72-83 (2016) - [j1]Erwan Scornet:
Random Forests and Kernel Methods. IEEE Trans. Inf. Theory 62(3): 1485-1500 (2016) - [i1]Gérard Biau, Erwan Scornet, Johannes Welbl:
Neural Random Forests. CoRR abs/1604.07143 (2016)
Coauthor Index
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