Our paper, KurtHGR: A Neural Maximal Correlation for Tabular Datasets, with Samuel Stocksieker and Denys Pommeret has been published in Procedia Computer Science The study of dependencies between variables is a fundamental pillar of machine learning, influencing areas as diverse as feature selection, fairness, dimensionality reduction, and multimodal learning. Among nonlinear correlation measures, the Hirschfeld-Gebelein-Rényi (HGR) maximal correlation stands out for its universality and remarkable theoretical properties. Defined as the maximum achievable correlation between nonlinear …