Multi-Task Learning of Gaussian Graphical Models

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Multi-Task Learning of Gaussian Graphical Models
We present multi-task structure learning for Gaussian graphical models. We discuss uniqueness and boundedness of the optimal solution of the maximization problem. A block coordinate descent method leads to a provably convergent algorithm that generates a sequence of positive definite solutions. Thus, we reduce the original problem into a sequence of strictly convex regularized quadratic minimization subproblems. We further show that this subproblem leads to the continuous quadratic knapsack problem, for which very efficient methods exist. Finally, we show promising results in a dataset that captures brain function of cocaine addicted and control subjects under conditions of monetary reward.
Jean Honorio, Dimitris Samaras
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors Jean Honorio, Dimitris Samaras
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