Graphical Lasso

Graphical Lasso is a statistical method used to estimate the structure of a Gaussian graphical model, representing conditional dependencies between variables as a graph. Current research focuses on improving the accuracy and scalability of Graphical Lasso, addressing challenges like high dimensionality, noisy data, and the need for fairness in model estimations. This involves developing novel algorithms, such as those based on difference-of-convex functions or proximal gradient methods, and incorporating structural assumptions like modularity or hub nodes to enhance model interpretability and efficiency. These advancements are impacting diverse fields, enabling more accurate and insightful analyses of complex datasets in areas such as neuroscience, genomics, and social networks.