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Many Machine Learning algorithms are formulated as regularized optimizationproblems, but their performance hinges on a regularization parameter that needsto be calibrated to each application at hand. In this paper, we propose ageneral calibration scheme for regularized optimization problems and apply itto the graphical lasso, which is a method for Gaussian graphical modeling. Thescheme is equipped with theoretical guarantees and motivates a thresholdingpipeline that can improve graph recovery. Moreover, requiring at most one linesearch over the regularization path, the calibration scheme is computationallymore efficient than competing schemes that are based on resampling. Finally, weshow in simulations that our approach can improve on the graph recovery ofother approaches considerably.

Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery