Scholar
Erwan Scornet
Google Scholar ID: 6Qt1NFoAAAAJ
Professeur, Sorbonne Université
Statistique
Machine Learning
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Citations & Impact
All-time
Citations
7,398
H-index
23
i10-index
30
Publications
20
Co-authors
0
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Publications
11 items
Principled Federated Random Forests for Heterogeneous Data
2026
Cited
0
Privacy Amplification by Missing Data
2026
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0
When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values
2025
Cited
0
How to rank imputation methods?
2025
Cited
0
Asymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classification
2025
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0
Causal Meta-Analysis: Rethinking the Foundations of Evidence-Based Medicine
2025
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A Unified Framework for the Transportability of Population-Level Causal Measures
2025
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Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring
2025
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Resume (English only)
Academic Achievements
Winner of the Jacques Neveu 2016 Prize for a thesis in probability or statistics.
Supervised numerous PhD students, co-supervised with researchers such as Stéphane Gaïffas, Julie Josse, Gaël Varoquaux, Claire Boyer, Aymeric Dieuleveut, and Emmanuel Malherbe.
Published influential papers including:
— 'Consistency of random forests' (Annals of Statistics, 2015)
— 'On the asymptotics of random forests' (Journal of Multivariate Analysis, 2016)
— 'Random forests and kernel methods' (IEEE Transactions on Information Theory, 2016)
— 'A Random Forest Guided Tour' (TEST, 2016)
— Multiple preprints (2023–2025) on missing data, causal inference, SMOTE variants, and random forest theory.
Background
Professor (lecturer) at LPSM and SCAI, Sorbonne Université since September 2023.
Previously assistant professor at the Center for Applied Mathematics (CMAP), École Polytechnique near Paris.
Research focuses on theoretical statistics and machine learning, with emphasis on nonparametric estimation.
PhD thesis on random forests, a machine learning algorithm.
Keywords: statistical learning, non-parametric estimation, random forests, decision trees, variable importance, missing data, neural networks, causal inference.
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