Experiments in Stochastic Computation for High-Dimensional Graphical Models

By Beatrix Jones, Carlos M. Carvalho, A. Dobra, Chris Hans, Chris Carter, Matthew West. 2005

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First-Order Methods for Sparse Covariance Selection (2006, background): “1) is convex and can readily be solved using interior point methods (see [4] for example).”
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces (2004, result): “The first option is done using the LASSO by Meinshausen and Buhlmann (2006). We now present similar results using the horseshoe.”
High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics (2008, methodology): “…stochastic search approach for regression variable selection (Hans et al. 2007) that has proven successful in graphical modeling (Dabra et al. 2004; Jones et al. 2005) and a range of prognostic applications in “large p ” regression variable uncertainty problems in gene expression genomics (Rich et…”
Handling Sparsity via the Horseshoe (2009, result): “The first option is done using the LASSO by Meinshausen and Buhlmann (2006). We now present similar results using the horseshoe.”