2 citations · 2 across the 11 of their papers we have counts for
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Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data
Ferdinand Genans, Erwan Scornet
Stochastic gradient methods are central to modern large-scale learning, but their use with incomplete covariates remains delicate since imputation schemes generally introduce syste…
Privacy Amplification by Missing Data
Simon Roburin, Rafaël Pinot, Erwan Scornet
Privacy preservation is a fundamental requirement in many high-stakes domains such as medicine and finance, where sensitive personal data must be analyzed without compromising indi…
Principled Federated Random Forests for Heterogeneous Data
Rémi Khellaf, Erwan Scornet, Aurélien Bellet +1
Random Forests (RF) are among the most powerful and widely used predictive models for centralized tabular data, yet few methods exist to adapt them to the federated learning settin…
When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values
Christophe Muller, Erwan Scornet, Julie Josse
Predicting with missing inputs challenges even parametric models, as parameter estimation alone is insufficient for prediction on incomplete data. While several works study predict…
Asymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classification
Moria Mayala, Erwan Scornet, Charles Tillier +1
Many classification tasks involve imbalanced data, in which a class is largely underrepresented. Several techniques consists in creating a rebalanced dataset on which a classifier…
Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
Abdoulaye Sakho, Emmanuel Malherbe, Erwan Scornet
Synthetic Minority Oversampling Technique (SMOTE) is a common rebalancing strategy for handling imbalanced tabular data sets. However, few works analyze SMOTE theoretically. In thi…