Probabilistic graphical models are used as graphical presentations of probability distributions. Graphical models have broad applications in the fields of biology, social science, linguistic, neuro-science, etc. In this talk, we will focus on graphical model structure learning under the high dimensional regime, where to avoid over-fitting and to develop computationally efficient algorithms are...