Erwan Scornet - RankMe

36 papers A* 8A 5Journal 21Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Principled Federated Random Forests for Heterogeneous Data. CoRR Rémi Khellaf, Erwan Scornet, Aurélien Bellet, Julie Josse
2026	J	`jnl`	Privacy Amplification by Missing Data. CoRR Simon Roburin, Rafaël Pinot, Erwan Scornet
2025	A	`conf`	A primer on linear classification with missing data. AISTATS Angel David Reyero Lobo, Alexis Ayme, Claire Boyer, Erwan Scornet
2025	J	`jnl`	Asymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classification. CoRR Moria Mayala, Erwan Scornet, Charles Tillier, Olivier Wintenberger
2025	—	`conf`	Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring. ECML/PKDD (9) Abdoulaye Sakho, Emmanuel Malherbe, Carl-Erik Gauthier, Erwan Scornet
2025	J	`jnl`	Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring. CoRR Abdoulaye Sakho, Emmanuel Malherbe, Carl-Erik Gauthier, Erwan Scornet
2025	A*	`conf`	Quantifying Treatment Effects: Estimating Risk Ratios via Observational Studies. ICML Ahmed Boughdiri, Julie Josse, Erwan Scornet
2025	J	`jnl`	When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values. CoRR Christophe Muller, Erwan Scornet, Julie Josse
2024	A*	`conf`	Random features models: a way to study the success of naive imputation. ICML Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet
2024	J	`jnl`	Theoretical and experimental study of SMOTE: limitations and comparisons of rebalancing strategies. CoRR Abdoulaye Sakho, Erwan Scornet, Emmanuel Malherbe
2023	A	`conf`	Is interpolation benign for random forest regression? AISTATS Ludovic Arnould, Claire Boyer, Erwan Scornet
2023	A*	`conf`	Naive imputation implicitly regularizes high-dimensional linear models. ICML Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet
2023	A*	`conf`	Sparse tree-based Initialization for Neural Networks. ICLR Patrick Lutz, Ludovic Arnould, Claire Boyer, Erwan Scornet
2022	J	`jnl`	Minimax rate of consistency for linear models with missing values. CoRR Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet
2022	A*	`conf`	Near-optimal rate of consistency for linear models with missing values. ICML Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet
2022	A	`conf`	SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. AISTATS Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet
2022	J	`jnl`	Sparse tree-based initialization for neural networks. CoRR Patrick Lutz, Ludovic Arnould, Claire Boyer, Erwan Scornet
2021	A*	`conf`	Analyzing the tree-layer structure of Deep Forests. ICML Ludovic Arnould, Claire Boyer, Erwan Scornet
2021	A	`conf`	Interpretable Random Forests via Rule Extraction. AISTATS Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet
2021	J	`jnl`	MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA. CoRR Clément Bénard, Sébastien Da Veiga, Erwan Scornet
2021	J	`jnl`	SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. CoRR Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet
2021	A*	`conf`	What's a good imputation to predict with missing values? NeurIPS Marine Le Morvan, Julie Josse, Erwan Scornet, Gaël Varoquaux
2021	J	`jnl`	What's a good imputation to predict with missing values? CoRR Marine Le Morvan, Julie Josse, Erwan Scornet, Gaël Varoquaux
2020	J	`jnl`	Analyzing the tree-layer structure of Deep Forests. CoRR Ludovic Arnould, Claire Boyer, Erwan Scornet
2020	J	`jnl`	Interpretable Random Forests via Rule Extraction. CoRR Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet
2020	A	`conf`	Linear predictor on linearly-generated data with missing values: non consistency and solutions. AISTATS Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gaël Varoquaux
2020	J	`jnl`	Linear predictor on linearly-generated data with missing values: non consistency and solutions. CoRR Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gaël Varoquaux
2020	A*	`conf`	NeuMiss networks: differentiable programming for supervised learning with missing values. NeurIPS Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux
2020	J	`jnl`	Neumann networks: differential programming for supervised learning with missing values. CoRR Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux
2019	J	`jnl`	AMF: Aggregated Mondrian Forests for Online Learning. CoRR Jaouad Mourtada, Stéphane Gaïffas, Erwan Scornet
2019	J	`jnl`	On the consistency of supervised learning with missing values. CoRR Julie Josse, Nicolas Prost, Erwan Scornet, Gaël Varoquaux
2019	J	`jnl`	SIRUS: making random forests interpretable. CoRR Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet
2017	—	`conf`	Universal consistency and minimax rates for online Mondrian Forests. NIPS Jaouad Mourtada, Stéphane Gaïffas, Erwan Scornet
2016	J	`jnl`	Neural Random Forests. CoRR Gérard Biau, Erwan Scornet, Johannes Welbl
2016	J	`jnl`	On the asymptotics of random forests. J. Multivar. Anal. Erwan Scornet
2016	J	`jnl`	Random Forests and Kernel Methods. IEEE Trans. Inf. Theory Erwan Scornet