A Sparse-Group Lasso

By Noah Simon, Jerome H. Friedman, Trevor J. Hastie, Robert Tibshirani. 2013

1,466 citations recorded by the source corpus.

Subfields: Modeling and Simulation, Rheumatology, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Modeling and Simulation

In Rheumatology

In Statistics and Probability

Citations in the corpus

Learning the Number of Neurons in Deep Networks (2016, methodology): “Following the derivations of [Simon et al., 2013], and focusing on the regularizer of Eq.”
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, methodology): “This minimization is known as Sparse Group Lasso (SGL) [171], [172].”
Model confidence bounds for variable selection (2016, background): “Yang et al. (2011), elastic net Zou and Hastie (2005); De Mol et al. (2009), adaptive Lasso Zou (2006); Zhang and Lu (2007); Huang et al. (2008), group Lasso Yuan and Lin (2006); Meier et al. (2008); Simon et al. (2013) and mini-max convex penalization (MCP) Jiang et al. (2013). Regardless of the se”
Bayesian Sparse Group Selection (2016, methodology): “First, Simon et al. (2013) suggested an automatic tuning procedure for λ via five-fold crossvalidation by fixing α = 0.95.”
Automatic node selection for Deep Neural Networks using Group Lasso regularization (2016, methodology): “Seeking such development, we propose in this paper a new training scheme that embeds group Lasso (gLasso) regularization [7, 8] for hidden layer weight vectors.”
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis (2013, methodology): “Sparse group lasso [16] is a special case of SOSlasso that only applies to disjoint sets, a signiﬁcant limitation when features cannot be easily partitioned, as is the case of our motivating example in fMRI.”
Reborn Filters: Pruning Convolutional Neural Networks with Limited Data (2020, background): “(11) is a group fused lasso problem (Simon et al. 2013), which can be solved by many off-the-shelf convex toolboxes, such as CVX (Grant and Boyd 2014) and SPAMS (Mairal et al. 2010).”
Group Regularized Estimation Under Structural Hierarchy (2014, background): “Without it, the variable groups can be shown to follow a tree structure, for which efficient algorithms can be developed on Jenatton et al. (2011) or Simon et al. (2013).”
Machine Learning Advances for Time Series Forecasting (2020, methodology): “The sparse group LASSO recover sparse groups by combining the group LASSO penalty with the 𝐿 1 penalty on the parameter vector (Simon et al., 2013).”
Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations (2015, positive): “For example, the sparse group lasso penalty, ∑K k=1 wk‖βgk‖2 +‖β‖1 (Simon et al., 2013) is a GL penalty with K + p groups: g1, . . . , gK, {1}, . . . , {p}.”
Neural Granger Causality (2021, background): “This penalty is a related to, and is a generalization of, the sparse group lasso [49].”
Group sparse regularization for deep neural networks (2016, methodology): “To force this, we also consider the following composite ‘sparse group Lasso’ (SGL) penalty [25], [26]: The SGL penalty has the same properties as its constituting norms, namely, it is convex but non-differentiable.”