4 citations · 4 across the 6 of their papers we have counts for
10 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…
Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling
Jules Désir, Vincent Auriau, Martin Možina +1
Brick-and-mortar retailers face many different challenges that involve understanding thoroughly its products catalog and customer preferences. In particular, assortment optimizatio…
Weak Segmentation-Guided GAN for~Realistic Color Edition
Vincent Auriau, Emmanuel Malherbe, Matthieu Perrot
Editing the color of images in a realistic way finds many applications such as changing the perception of an image, data augmentation or film post processing. The design of an auto…
OT-FairBoost: Optimal Transport-Guided Gradient Boosting for Fairness Regularization on Tabular Data
Veronika Shilova, Abdoulaye Sakho, Younes Boumoussou +3
Although neural-based machine learning models have received a lot of attention recently, tree-based models such as gradient boosting are competitive for tabular data and therefore…
Discovering Geometric Biases in 3D Face Reconstruction: A Curvature-Aware Spectral Framework for Fairness Evaluation
Veronika Shilova, Emmanuel Malherbe, Giovanni Palma +3
3D Morphable Models (3DMMs) remain the standard parametric shape priors for many state-of-the-art 3D face reconstruction algorithms. However, as these models are derived from a fin…
Identifying two piecewise linear additive value functions from anonymous preference information
Vincent Auriau, Khaled Belahcene, Emmanuel Malherbe +2
Eliciting a preference model involves asking a person, named decision-maker, a series of questions. We assume that these preferences can be represented by an additive value functio…
Learned Hallucination Detection in Black-Box LLMs using Token-level Entropy Production Rate
Charles Moslonka, Hicham Randrianarivo, Arthur Garnier +1
Hallucinations in Large Language Model (LLM) outputs for Question Answering (QA) tasks can critically undermine their real-world reliability. This paper introduces a methodology fo…
Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone
Veronika Shilova, Emmanuel Malherbe, Giovanni Palma +2
Within a legal framework, fairness in datasets and models is typically assessed by dividing observations into predefined groups and then computing fairness measures (e.g., Disparat…
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…
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…