599 citations · 862 across the 35 of their papers we have counts for
41 papers
Weighting-Based Strategies for Classification: Analysis and Local Peak Dynamics with CART Trees
Abdoulaye Sakho, Emmanuel Malherbe, Erwan Scornet
Tabular learning tasks often suffer from class imbalance, which is often addressed via weighting-based strategies. In this work, we compare the naive approach (unweighted) with the…
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…
On the Inherent Privacy Amplification of Missing Data
Simon Roburin, Rafaël Pinot, Rafa{ë}l Pinot +1
Privacy preservation is critical in many high-stakes domains such as medicine and finance, where sensitive data must be analyzed without compromising individual confidentiality. At…
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…
How to rank imputation methods?
Jeffrey Näf, Krystyna Grzesiak, Erwan Scornet
Imputation is an attractive tool for dealing with the widespread issue of missing values. Consequently, studying and developing imputation methods has been an active field of resea…
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…
A Unified Framework for the Transportability of Population-Level Causal Measures
Ahmed Boughdiri, Clément Berenfeld, Julie Josse +1
Generalization methods offer a powerful solution to one of the key drawbacks of randomized controlled trials (RCTs): their limited representativeness. By enabling the transport of…
Causal Meta-Analysis: Rethinking the Foundations of Evidence-Based Medicine
Clément Berenfeld, Ahmed Boughdiri, Bénédicte Colnet +5
Meta-analysis, by synthesizing effect estimates from multiple studies conducted in diverse settings, stands at the top of the evidence hierarchy in clinical research. Yet, conventi…
Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring
Abdoulaye Sakho, Emmanuel Malherbe, Carl-Erik Gauthier +1
This study investigates rare event detection on tabular data within binary classification. Standard techniques to handle class imbalance include SMOTE, which generates synthetic sa…
Quantifying Treatment Effects: Estimating Risk Ratios in Causal Inference
Ahmed Boughdiri, Julie Josse, Erwan Scornet
Randomized Controlled Trials (RCT) are the current gold standards to empirically measure the effect of a new drug. However, they may be of limited size and resorting to complementa…
A primer on linear classification with missing data
Angel D Reyero Lobo, Alexis Ayme, Claire Boyer +1
Supervised learning with missing data aims at building the best prediction of a target output based on partially-observed inputs. Major approaches to address this problem can be de…
What Is a Good Imputation Under MAR Missingness?
Jeffrey Näf, Erwan Scornet, Julie Josse
Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various field…
Random features models: a way to study the success of naive imputation
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut +1
Constant (naive) imputation is still widely used in practice as this is a first easy-to-use technique to deal with missing data. Yet, this simple method could be expected to induce…
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…
Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?
Bénédicte Colnet, Julie Josse, Gaël Varoquaux +1
There are many measures to report so-called treatment or causal effects: absolute difference, ratio, odds ratio, number needed to treat, and so on. The choice of a measure, e.g. ab…
Naive imputation implicitly regularizes high-dimensional linear models
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut +1
Two different approaches exist to handle missing values for prediction: either imputation, prior to fitting any predictive algorithms, or dedicated methods able to natively incorpo…
Sparse tree-based initialization for neural networks
Patrick Lutz, Ludovic Arnould, Claire Boyer +1
Dedicated neural network (NN) architectures have been designed to handle specific data types (such as CNN for images or RNN for text), which ranks them among state-of-the-art metho…
Reweighting the RCT for generalization: finite sample error and variable selection
Bénédicte Colnet, Julie Josse, Gaël Varoquaux +1
Randomized Controlled Trials (RCTs) may suffer from limited scope. In particular, samples may be unrepresentative: some RCTs over- or under- sample individuals with certain charact…
Minimax rate of consistency for linear models with missing values
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut +1
Missing values arise in most real-world data sets due to the aggregation of multiple sources and intrinsically missing information (sensor failure, unanswered questions in surveys.…
Is interpolation benign for random forest regression?
Ludovic Arnould, Claire Boyer, Erwan Scornet
Statistical wisdom suggests that very complex models, interpolating training data, will be poor at predicting unseen examples.Yet, this aphorism has been recently challenged by the…
What's a good imputation to predict with missing values?
Marine Le Morvan, Julie Josse, Erwan Scornet +1
How to learn a good predictor on data with missing values? Most efforts focus on first imputing as well as possible and second learning on the completed data to predict the outcome…
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Clément Bénard, Gérard Biau, Sébastien da Veiga +1
Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools. Shapley effects a…
Causal effect on a target population: a sensitivity analysis to handle missing covariates
Bénédicte Colnet, Julie Josse, Erwan Scornet +1
Randomized Controlled Trials (RCTs) are often considered the gold standard for estimating causal effect, but they may lack external validity when the population eligible to the RCT…