Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Classifying matrices with a spectral regularization
Proceedings of the 24th international conference on Machine learning
The Journal of Machine Learning Research
Consistency of Trace Norm Minimization
The Journal of Machine Learning Research
Covariance selection for nonchordal graphs via chordal embedding
Optimization Methods & Software - Mathematical programming in data mining and machine learning
Convex multi-task feature learning
Machine Learning
High Dimensional Inverse Covariance Matrix Estimation via Linear Programming
The Journal of Machine Learning Research
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The paper proposes a joint convex penalty for estimating the Gaussian inverse covariance matrix. A proximal gradient method is developed to solve the resulting optimization problem with more than one penalty constraints. The analysis shows that imposing a single constraint is not enough and the estimator can be improved by a trade-off between two convex penalties. The developed framework can be extended to solve wide arrays of constrained convex optimization problems. A simulation study is carried out to compare the performance of the proposed method to graphical lasso and the SPICE estimate of the inverse covariance matrix.