3 papers
cs.LG2025
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…
cs.LG2024
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…
cs.LG2024
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…