3 citations · 7 across the 4 of their papers we have counts for
5 papers
Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression
Samuel Stocksieker, Denys pommeret, Arthur Charpentier
Imbalanced distribution learning is a common and significant challenge in predictive modeling, often reducing the performance of standard algorithms. Although various approaches ad…
Data Augmentation with Variational Autoencoder for Imbalanced Dataset
Samuel Stocksieker, Denys Pommeret, Arthur Charpentier
Learning from an imbalanced distribution presents a major challenge in predictive modeling, as it generally leads to a reduction in the performance of standard algorithms. Various…
Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets
Samuel Stocksieker, Denys Pommeret, Arthur Charpentier
The field of imbalanced self-supervised learning, especially in the context of tabular data, has not been extensively studied. Existing research has predominantly focused on image…
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…
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…