GAP Safe Screening Rules for Sparse-Group Lasso

NeurIPS 2016 Eugene NdiayeOlivier FercoqAlexandre GramfortJoseph Salmon

For statistical learning in high dimension, sparse regularizations have proven useful to boost both computational and statistical efficiency. In some contexts, it is natural to handle more refined structures than pure sparsity, such as for instance group sparsity... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper