The Large Hadron Collider has provided us with an enormous amount of data and an arguably larger challenge by confirming to astounding precision the Standard Model of particle physics. In this talk, we detail some possible avenues that take advantage of the former to increase the power of statistical tests over the later. These new avenues aim to adapt existing Machine Learning methods to the domain specific problems phenomenological studies face, and can range from supervised to unsupervised searches.