Author

J. Loubes

6 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Aug 2026

Using ambient AI in clinical consultations: reframing policy around clinical audit and patient safety

Recent work has mapped what is legally permitted, commercially practiced, and technically coming for ambient AI in clinical care, yet it has not addressed the prior question of what the recording is for. This Perspective offers a policy argument, not an empirical evaluation. Documentation, workflow efficiency, patient...

V. Misrai, A. Bruchon, P. Dasgupta et al. · Frontiers in Digital Health · 0 citations

#small language model Preprint Oct 2026

On the Comparison of Optimizers for Imbalanced Learning

This work studies idealized optimizers geometries in continuous time to model small-step training in deep learning and characterize a region where majority losses are optimized regardless of the admissible minority structure.

Clément Lezane, François Bachoc, Jérôme Bolte et al. · 0 citations

#large language models Book Open access Oct 2026

A Methodology to Compare Real and LLM-Simulated Multimodal Interactions: The Case of Gender Bias

Large Language Models (LLMs) are increasingly used to generate multimodal data for building corpora or simulating synthetic behaviours. A key challenge remains the evaluation of generated data. Comparison with real multimodal human interactions may help address this issue, but unified representations and evaluation fra...

E. Etienne, M. Ochs, J. Loubes et al. · 0 citations

#artificial intelligence May 2025

When majority rules, minority loses: bias amplification of gradient descent

A formal framework for majority-minority learning tasks is developed, showing how standard training can favor majority groups and produce stereotypical predictors that neglect minority-specific features.

François Bachoc, Jérôme Bolte, Ryan Boustany et al. · Neural Information Processin... · 3 citations

Preprint Jul 2026

OT-FairBoost: Optimal Transport-Guided Gradient Boosting for Fairness Regularization on Tabular Data

OT-FairBoost is introduced, a novel in-processing framework that incorporates a Wasserstein-2 distance penalty directly into the objective function of gradient-boosted trees to efficiently optimize group fairness criteria such as Demographic Parity and Equalized Odds on neural-based predictions.

Veronika Shilova, Abdoulaye Sakho, Younes Boumoussou et al. · 0 citations

#machine learning Mar 2026

Probing Cultural Signals in Large Language Models through Author Profiling

Across several open-source models evaluated on more than 10,000 lyrics, it is found that LLMs achieve non-trivial profiling performance but demonstrate systematic cultural alignment: most models default toward North American ethnicity, while DeepSeek-1.5B aligns more strongly with Asian ethnicity.

Valentin Lafargue, A. Guerra-Adames, E. Claeys et al. · arXiv.org · 0 citations