Sparse Regression

Sparse regression aims to identify a small subset of relevant variables from a large dataset that best explains a given outcome, prioritizing model interpretability and efficiency. Current research focuses on improving the robustness and scalability of sparse regression methods, particularly in high-dimensional settings, through techniques like LASSO, Elastic Net regularization, and advanced algorithms such as ADMM and the Difference of Convex functions Algorithm (DCA). These advancements are impacting diverse fields, enabling more accurate predictive modeling in areas like agriculture, finance, and scientific discovery by facilitating the extraction of governing equations from complex datasets. Furthermore, research is exploring efficient distributed and online implementations to handle increasingly large and streaming data.