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.

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Jean Honorio, Dimitris Samaras
• Keywords: ICML 2010, Machine Learning, Maximization Problem, Quadratic Knapsack Problem, Regularized Quadratic Minimization