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Activity Number: 615
Type: Invited
Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
Sponsor: JCGS-Journal of Computational and Graphical Statistics
Abstract - #307135
Title: A Sparse-Group Lasso, Computation, and GPUs
Author(s): Noah Simon*+
Companies: Stanford University
Keywords: GPU ; Lasso ; High Dimensional ; Convex Optimization ; Penalized Regression
Abstract:

In this age of massive data acquisition, penalized regression provides a nice framework for applying necessary structural assumptions. For example, in regression our features may have a natural grouping (eg. indicators for categorical variables). We may want to find a model which both restricts our active features to only a few groups, and removes unnecessary variables from within those groups. Toward this end we have proposed a Sparse-group Lasso, a tool from penalized regression which combines Lasso and Group Lasso penalties. Efficient optimization is key for applying the Sparse-group Lasso to high dimensional problems. In this talk I will discuss the Sparse-group Lasso and its optimization. In particular I will illustrate the role that GPUs can play in convex optimization for structured sparsity.


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