Our paper, Data Augmentation with Variational Autoencoder for Imbalanced Dataset, with Samuel Stocksieker and Denys Pommeret is now online on ArXiv. 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 approaches exist to address this issue, but many of them concern classification problems, with a limited focus on regression. In this paper, we introduce a novel method aimed at enhancing learning on tabular data in the Imbalanced … <a href=“https://freakonometrics …