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Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models
[article]
2015
arXiv
pre-print
This paper addresses the problem of scalable optimization for L1-regularized conditional Gaussian graphical models. Conditional Gaussian graphical models generalize the well-known Gaussian graphical models to conditional distributions to model the output network influenced by conditioning input variables. While highly scalable optimization methods exist for sparse Gaussian graphical model estimation, state-of-the-art methods for conditional Gaussian graphical models are not efficient enough and
arXiv:1509.04681v2
fatcat:dzrch7lfo5adhbtnmxk5ul3rey