Our paper, Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression, with Samuel Stocksieker and Denys Pommeret has been published in Procedia Computer Science Imbalanced distribution learning is a common and significant challenge in predictive modeling, often reducing the performance of standard algorithms. Although various approaches address this issue, most are tailored to classification problems, with a limited focus on regression. This paper introduces a novel method to improve learning on tabular data within the Imbalanced Regression (IR) framework, which is a critical …