The non-overlapping statistical approximation to overlapping group lasso. (arXiv:2211.09221v1 [stat.ML])

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 this paper, we propose a …

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