5 papers
Tessellation Localized Transfer learning for nonparametric regression
Hélène Halconruy, Benjamin Bobbia, Paul Lejamtel
Transfer learning aims to improve performance on a target task by leveraging information from related source tasks. We propose a nonparametric regression transfer learning framewor…
Variance-reduced extreme value index estimators using control variates in a semi-supervised setting
Louison Bocquet-Nouaille, Jérôme Morio, Benjamin Bobbia
The estimation of the Extreme Value Index (EVI) is fundamental in extreme value analysis but suffers from high variance due to reliance on only a few extreme observations. We propo…
Control variates for variance-reduced ratio of means estimators
Louison Bocquet-Nouaille, Jérôme Morio, Benjamin Bobbia
The control variates method is a classical variance reduction technique for Monte Carlo estimators that exploits correlated auxiliary variables without introducing bias. In many ap…
Do you understand epistemic uncertainty? Think again! Rigorous frequentist epistemic uncertainty estimation in regression
Enrico Foglia, Benjamin Bobbia, Nikita Durasov +4
Quantifying model uncertainty is critical for understanding prediction reliability, yet distinguishing between aleatoric and epistemic uncertainty remains challenging. We extend re…
Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks
Benjamin Bobbia, Matthias Picard
This paper addresses a new active learning strategy for regression problems. The presented Wasserstein active regression model is based on the principles of distribution-matching t…