Regularization techniques are widely used for tackling high-dimension-low-sample-size problems. Yet, finding the right amount of regularization is challenging, especially in the unsupervised setting,  where traditional methods such as BIC or cross-validation often  result in too many false positives. In this talk,  we first introduce Gaussian graphical models (GGMs)  and its inference under...