Neural Random Forests. - Gérard Biau; Erwan Scornet; Johannes Welbl (CoRR, 2016)

On the asymptotics of random forests. - Erwan Scornet (J. Multivar. Anal., 2016)

Random Forests and Kernel Methods. - Erwan Scornet (IEEE Trans. Inf. Theory, 2016)

Universal consistency and minimax rates for online Mondrian Forests. - Jaouad Mourtada; Stéphane Gaïffas; Erwan Scornet (NIPS, 2017)

On the consistency of supervised learning with missing values. - Julie Josse; Nicolas Prost; Erwan Scornet; Gaël Varoquaux (CoRR, 2019)

AMF: Aggregated Mondrian Forests for Online Learning. - Jaouad Mourtada; Stéphane Gaïffas; Erwan Scornet (CoRR, 2019)

SIRUS: making random forests interpretable. - Clément Bénard; Gérard Biau; Sébastien Da Veiga; Erwan Scornet (CoRR, 2019)

Linear predictor on linearly-generated data with missing values: non consistency and solutions. - Marine Le Morvan; Nicolas Prost; Julie Josse; Erwan Scornet; Gaël Varoquaux (AISTATS, 2020)

NeuMiss networks: differentiable programming for supervised learning with missing values. - Marine Le Morvan; Julie Josse; Thomas Moreau 001; Erwan Scornet; Gaël Varoquaux (NeurIPS, 2020)

Neumann networks: differential programming for supervised learning with missing values. - Marine Le Morvan; Julie Josse; Thomas Moreau 001; Erwan Scornet; Gaël Varoquaux (CoRR, 2020)

Interpretable Random Forests via Rule Extraction. - Clément Bénard; Gérard Biau; Sébastien Da Veiga; Erwan Scornet (AISTATS, 2021)

Analyzing the tree-layer structure of Deep Forests. - Ludovic Arnould; Claire Boyer; Erwan Scornet (ICML, 2021)

What's a good imputation to predict with missing values? - Marine Le Morvan; Julie Josse; Erwan Scornet; Gaël Varoquaux (NeurIPS, 2021)

MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA. - Clément Bénard; Sébastien Da Veiga; Erwan Scornet (CoRR, 2021)

SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. - Clément Bénard; Gérard Biau; Sébastien Da Veiga; Erwan Scornet (AISTATS, 2022)

Near-optimal rate of consistency for linear models with missing values. - Alexis Ayme; Claire Boyer; Aymeric Dieuleveut; Erwan Scornet (ICML, 2022)

Minimax rate of consistency for linear models with missing values. - Alexis Ayme; Claire Boyer; Aymeric Dieuleveut; Erwan Scornet (CoRR, 2022)

Is interpolation benign for random forest regression? - Ludovic Arnould; Claire Boyer; Erwan Scornet (AISTATS, 2023)

Sparse tree-based Initialization for Neural Networks. - Patrick Lutz; Ludovic Arnould; Claire Boyer; Erwan Scornet (ICLR, 2023)

Naive imputation implicitly regularizes high-dimensional linear models. - Alexis Ayme; Claire Boyer; Aymeric Dieuleveut; Erwan Scornet (ICML, 2023)

Random features models: a way to study the success of naive imputation. - Alexis Ayme; Claire Boyer; Aymeric Dieuleveut; Erwan Scornet (ICML, 2024)

Theoretical and experimental study of SMOTE: limitations and comparisons of rebalancing strategies. - Abdoulaye Sakho; Erwan Scornet; Emmanuel Malherbe (CoRR, 2024)

A primer on linear classification with missing data. - Angel David Reyero Lobo; Alexis Ayme; Claire Boyer; Erwan Scornet (AISTATS, 2025)

Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring. - Abdoulaye Sakho; Emmanuel Malherbe; Carl-Erik Gauthier; Erwan Scornet (CoRR, 2025)

Asymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classification. - Moria Mayala; Erwan Scornet; Charles Tillier; Olivier Wintenberger (CoRR, 2025)

When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values. - Christophe Muller; Erwan Scornet; Julie Josse (CoRR, 2025)