In contrast to the group lasso problem, where features are grouped into disjoint blocks, it may be the case that grouped features are overlapping or have a nested structuresstructure. Such generalizations of group lasso have been considered in a variety of contexts.<ref>{{cite journal|last=Chen|first=X.|coauthors=Lin, Q., Kim, S., Carbonell, J.G., and Xing, E.P.|title=Smoothing proximal gradient method for general structured sparse regression|journal=Ann. Appl. Stat.|year=2012|volume=6|issue=2|pages=719-752|doi=10.1214/11-AOAS514}}</ref><ref>{{cite journal|last=Mosci|first=S.|coauthors=Villa, S., Verri, A., and Rosasco, L.|title=A primal-dual algorithm for group sparse regularization with overlapping groups|journal=NIPS|year=2010|volume=23|pages=2604-2612}}</ref><ref name=nest>{{cite journal|last=Jenatton|first=R.|coauthors=Audibert, J.-Y., and Bach, F.|title=Structured variable selection with sparsity-inducing norms|journal=J. Mach. Learn. Res.|year=2011|volume=12|pages=2777-2824}}</ref><ref>{{cite journal|last=Zhao|first=P.|coauthors=Rocha, G., and Yu, B.|title=The composite absolute penalties family for grouped and hierarchical variable selection|journal=Ann. Statist.|year=2009|volume=37|issue=6A|pages=3468-3497|doi=10.1214/07-AOS584}}</ref> For overlapping groups one common approach is known as ''latent group lasso'' which introduces latent variables to account for overlap.<ref>{{cite journal|last=Obozinski|first=G.|coauthors=Laurent, J., and Vert, J.-P.|title=Group lasso with overlaps: the latent group lasso approach|journal=INRIA Technical Report|year=2011|url=http://hal.inria.fr/inria-00628498/en/}}</ref><ref>{{cite journal|last=Villa|first=S.|coauthors=Rosasco, L., Mosci, S., and Verri, A.|title=Proximal methods for the latent group lasso penalty|journal=preprint|year=2012|url=http://arxiv.org/abs/1209.0368}}</ref> Nested group structures are studied in ''hierarchical structure prediction'' and with [[Directed_acyclic_graph|directed acyclic graphs]].<ref name=nest />
