30 citations · 74 across the 20 of their papers we have counts for
4 papers · 2 filters
Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory
Samuel Stocksieker, Denys Pommeret, Arthur Charpentier
In supervised learning, it is quite frequent to be confronted with real imbalanced datasets. This situation leads to a learning difficulty for standard algorithms. Research and sol…
Fairness in Multi-Task Learning via Wasserstein Barycenters
François Hu, Philipp Ratz, Arthur Charpentier
Algorithmic Fairness is an established field in machine learning that aims to reduce biases in data. Recent advances have proposed various methods to ensure fairness in a univariat…
Mitigating Discrimination in Insurance with Wasserstein Barycenters
Arthur Charpentier, François Hu, Philipp Ratz
The insurance industry is heavily reliant on predictions of risks based on characteristics of potential customers. Although the use of said models is common, researchers have long…
Data Augmentation for Imbalanced Regression
Samuel Stocksieker, Denys Pommeret, Arthur Charpentier
In this work, we consider the problem of imbalanced data in a regression framework when the imbalanced phenomenon concerns continuous or discrete covariates. Such a situation can l…