Proximal Projection for Doubly Sparse Regularized Models

ArXi:2605.05093v1 Announce Type: cross Regularization is often used in high-dimensional regression settings to generate a sparse model, which can save tremendous computing resources and identify predictors that are most strongly associated with the response. When the predictors can be represented by a Gaussian graphical model, the structure of the predictor graph can be exploited during regularization.