High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression

By Bernhard Schölkopf, John C. Platt, Thomas Hofmann. 2006

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Citations in the corpus

Learning generative models for protein fold families (2011, methodology): “Instead of approximating the true-likelihood using approximate inference techniques, we use a different approximation based on a pseudo-likelihood proposed by [16], and used in [17, 18].”
Sparse additive models (2007, background): “In the parametric setting, one result in this direction is Wainwright et al. (2007).”
Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity (2009, methodology): “, a gene expression measurement for a single gene), techniques such as lasso (Tibshirani, 1996) or L1-regularized logistic regression (Ng, 2004; Wainwright et al., 2006) have been developed to identify a parsimonious subset of covariates that determine the outputs.”
On the Convexity of Latent Social Network Inference (2010, methodology): “Recently, grap hic l Lasso methods [25, 21, 6, 19] for static sparse graph estimation and extensions to time evolv ing graphical models [1, 8, 22] have been proposed with lots of success.”
1 Trend Filtering (2009, methodology): “The idea of l1 regularization has been used in other contexts including portfolio optimization [69], control design [49], computer-aided design of integrated circuits [14], decoding of linear codes [18], and machine learning (sparse principal component analysis [26] and graphical model selection [3,”
High-Dimensional Graphical Model Selection Using $\ell_1$-Regularized Logistic Regression (2008, background): “In work subsequent to the initial conference version of this work (Wainwright et al., 2007), other researchers have also studied the problem of model selection in discrete Markov random ﬁelds.”
Compressed and Privacy-Sensitive Sparse Regression (2009, methodology): “For instance, logistic regression with -regularization has recently been shown to be effective in isolating relevant variables in high-dimensional classification problems [36]; we expect that compressed logistic regression can be shown to have similar theoretical guarantees to those shown in the cur”
Fast alternating linearization methods for minimizing the sum of two convex functions (2009, unknown): “, [4,19,48,51]).”
Multi-Task Learning of Gaussian Graphical Models (2010, background): “…learning has been applied to very diverse problems, such as linear regression (Liu et al., 2009), classiﬁcation (Jebara, 2004), compressive sensing (Qi et al., 2008), reinforcement learning (Wilson et al., 2007) and structure learning of Bayesian networks (Niculescu-Mizil & Caruana, 2007).”
Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods (2009, methodology): “They show that under certain assumptions, their method correctly identifies the non-zero edges in a Markov graph, as N → ∞ even for increasing number of parameters p or neighborhood sizes of the graph d, as long as N grows more quickly than d 3 log p (see Wainwright et al., 2008).”
Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis. (2012, methodology): “Indeed it has been used successfully in learning logistic regression (Lee et al. 2006b) and in various graphical models (Lee et al. 2006a, Wainwright et al. 2007), as well as in point process models of neuronal spike trains (Kelly et al. 2010).”