High Dimensional Learning Report

The problem of high dimensional learning is considered. Efficient methods are developed for learning latent variable models and graphical models in high dimensions. Theoretical guarantees are established for the developed methods. The methods are applied to various domains including social networks and computational biology. (a) Papers published in peer-reviewed journals (N/A for none) Enter List of papers submitted or published that acknowledge ARO support from the start of the project to the date of this printing. List the papers, including journal references, in the following categories: