The non-overlapping statistical approximation to overlapping group lasso

arXiv:2211.09221v3 Announce Type: replace-cross
Abstract: Group lasso is a commonly used regularization method in statistical learning in which parameters are eliminated from the model according to predefined groups. However, when the groups overlap, optimizing the group lasso penalized objective can be time-consuming on large-scale problems because of the non-separability induced by the overlapping groups. This bottleneck has seriously limited the application of overlapping group lasso regularization in many modern problems, such as gene pathway selection and graphical model estimation. In …

abstract approximation arxiv cs.lg lasso parameters regularization scale statistical stat.ml type