Erwan Scornet

According to our database, Erwan Scornet authored at least 21 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Theoretical and experimental study of SMOTE: limitations and comparisons of rebalancing strategies.

Abdoulaye Sakho, Erwan Scornet, Emmanuel Malherbe

CoRR, 2024

2023

Naive imputation implicitly regularizes high-dimensional linear models.

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

Proceedings of the International Conference on Machine Learning, 2023

Sparse tree-based Initialization for Neural Networks.

Patrick Lutz, Ludovic Arnould, Claire Boyer, Erwan Scornet

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Is interpolation benign for random forest regression?

Ludovic Arnould, Claire Boyer, Erwan Scornet

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Minimax rate of consistency for linear models with missing values.

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

CoRR, 2022

Near-optimal rate of consistency for linear models with missing values.

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

Proceedings of the International Conference on Machine Learning, 2022

SHAFF: Fast and consistent SHApley eFfect estimates via random Forests.

Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA.

Clément Bénard, Sébastien Da Veiga, Erwan Scornet

CoRR, 2021

What's a good imputation to predict with missing values?

Marine Le Morvan, Julie Josse, Erwan Scornet, Gaël Varoquaux

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Analyzing the tree-layer structure of Deep Forests.

Ludovic Arnould, Claire Boyer, Erwan Scornet

Proceedings of the 38th International Conference on Machine Learning, 2021

Interpretable Random Forests via Rule Extraction.

Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Neumann networks: differential programming for supervised learning with missing values.

Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux

CoRR, 2020

NeuMiss networks: differentiable programming for supervised learning with missing values.

Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Linear predictor on linearly-generated data with missing values: non consistency and solutions.

Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gaël Varoquaux

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

SIRUS: making random forests interpretable.

Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet

CoRR, 2019

AMF: Aggregated Mondrian Forests for Online Learning.

Jaouad Mourtada, Stéphane Gaïffas, Erwan Scornet

CoRR, 2019

On the consistency of supervised learning with missing values.

Julie Josse, Nicolas Prost, Erwan Scornet, Gaël Varoquaux

CoRR, 2019

2017

Universal consistency and minimax rates for online Mondrian Forests.

Jaouad Mourtada, Stéphane Gaïffas, Erwan Scornet

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Random Forests and Kernel Methods.

Erwan Scornet

IEEE Trans. Inf. Theory, 2016

On the asymptotics of random forests.

Erwan Scornet

J. Multivar. Anal., 2016

Neural Random Forests.

Gérard Biau, Erwan Scornet, Johannes Welbl

CoRR, 2016