Distance Correlation

Distance correlation is a statistical measure quantifying the dependence between two random vectors, even in the absence of linear correlation. Current research focuses on its applications in diverse fields, including evaluating the quality of datasets for machine learning, improving knowledge distillation in neural networks, and analyzing the fitness landscapes of permutation problems. This versatile tool enhances model development by enabling better feature selection, improved model robustness, and a deeper understanding of model behavior and performance across various tasks. Its use in privacy-preserving statistical testing further highlights its broad significance.