From the 1 of 7 linked papers with an AI index.
7 papers
OT-FairBoost: Optimal Transport-Guided Gradient Boosting for Fairness Regularization on Tabular Data
Veronika Shilova, Abdoulaye Sakho, Younes Boumoussou +3
The paper proposes OT-FairBoost, an in-processing method that adds a Wasserstein-2 distance penalty to gradient-boosted tree training to improve group fairness while maintaining ac…
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…
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…
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…