Estimation Consistency of the Group Lasso and its Applications

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Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “Similarly, `1-regularization is only guaranteed to recover the correct sparse solution (i.e., the one that minimizes the `0-norm) under restrictive conditions that are rarely satisfied in practice (Zhao and Yu, 2007, Liu and Zhang, 2009).”
Methods and Models for Interpretable Linear Classification (2014, background): “the one that minimizes the number of non-zero coefficients) under very restrictive conditions that are rarely satisfied in practice [119, 70].”
SLEP: Sparse Learning with Efficient Projections (2011, background): “l1/lq-Norm Regularization Recent studies in areas such as machine learning and statist ics have also witnessed growing interests in extending the l1-regularization to thel1/lq-regularization [2, 3, 6, 10, 28, 29, 30, 31, 33, 40, 43, 49, 50, 53, 72, 73].”
Supersparse Linear Integer Models for Interpretable Classification (2013, background): “The (cid:96) 1 -regularization is only guaranteed to produce the correct sparse solution (the one which minimizes the (cid:96) 0 -norm) under very restrictive conditions that are rarely satisﬁed in practice (see [79, 44]).”
High Dimensional Semiparametric Gaussian Copula Graphical Models. (2012, methodology): “Using the same idea as sparse additive models [Liu, Lafferty and Wasserman (2008), Liu and Zhang (2009), Ravikumar et al.”