The group lasso for logistic regression

By Lukas Meier, Sara van de Geer, Peter Buhlmann. 2008

1,822 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Optimization & Training

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Citations in the corpus

FastText.zip: Compressing text classification models (2016, background): “While many approaches have been proposed to select groups of variables during training (Bach et al., 2012; Meier et al., 2008), we are interested in selecting a ﬁxed subset of K words and ngrams from a pre-trained model.”
Understanding Neural Networks through Representation Erasure (2016, methodology): “Such an idea is inspired by group lasso (Meier et al., 2008), which has been widely employed in many NLP tasks, such as document classification (Yogatama and Smith, 2014) and providing rationales for neural model interpretation (Lei et al.”
Sparse Partially Linear Additive Models (2014, methodology): “j;1=t0 j gand = minfC j;1=t0 j g. Thus, according to Theorem 2 in [37], Algorithm 1 converges Q-linearly. A.4 Practical Issues A.4.1 Active Set Strategy We employ the widely used active set strategy [39, 40, 41]. After a complete cycle through all the variables, we iterate only on the active set til”
Regularization Paths for Generalized Linear Models via Coordinate Descent (2010, background): “The grouped lasso (Yuan and Lin 2007; Meier et al. 2008), where variables are included or excluded in groups.”
Joint covariate selection and joint subspace selection for multiple classification problems (2010, methodology): “This approach is a particular case of a general methodology in which block norms are used to define groupings of variables in regression and classification problems (Bach et al. 2004; Yuan and Lin 2006; Park and Hastie 2006; Meier et al. 2008; Kim et al. 2006; Zhao et al. 2008).”
SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR (2008, methodology): “We refer to [9, 10, 20, 21, 24] and [16] for detailed discussion of these optimization problems and fast algorithms.”
Stability selection (2008, methodology): “, 2004) or fast co-ordinatewise approaches (Meier et al., 2008; Friedman et al., 2007).”
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function (2011, background): “(iv) There are many more choices such as the elastic net [32], group lasso [30], [10], [14] and sparse group lasso [4].”
On asymptotically optimal confidence regions and tests for high-dimensional models (2013, neutral): “ce of Θ can be done eﬃciently via coordinateˆ descent using the KKT conditions to characterize the zeroes. Furthermore, an active set strategy leads to additional speed-up. See, for example, [20] and [28]. For standard GLMs, the matrix input Σ=ˆ Pnρ¨ ˆ β in (17) can be written as Σ=ˆ Σˆ βˆ=X T βˆ Xβ”
High-dimensional additive modeling (2008, methodology): “Coordinate-wise approaches as in [21, 32] are efficient and have rigorous convergence properties.”
Multi-population GWA mapping via multi-task regularized regression (2010, methodology): “In each simulated dataset, we randomly select five SNPs with the same level of differentiation as causal SNPs, and show the results averaged over 50 datasets in Figure 6.”
Sparsistency and Rates of Convergence in Large Covariance Matrix Estimation (2007, methodology): “Such a group coordinate decent algorithm was also used by Meier, van de Geer and B¨uhlmann (2008) to solve the group LASSO problem.”
Gradient boosted feature selection (2014, methodology): “Similar to GBFS, Group Lasso with logistic regression can also deal with structured features.”
Regression shrinkage and selection via the lasso: a retrospective (2011, neutral): “A key work is Tseng (2001), and also Tseng and Yun (2009) is crucial for extending the computation to for example group lasso problems for the non-Gaussian, generalized linear model case (Meier et al., 2008).”
1-Bit Matrix Completion (2012, background): “A similar theory is available for the closely related problem of sparse binomial regression, which considers more classical statistical models [2, 11, 34, 46, 48, 52, 54, 65] and allows non-Gaussian measurements.”