Computer scientist Rong Ge has an interesting approach to machine learning. While most machine learning specialists will build an algorithm which molds to a specific dataset, Ge builds an algorithm which he can guarantee will perform well across many datasets. A paper he wrote as a postdoc at Microsoft Research, Escaping From Saddle Points—Online Stochastic Gradient for Tensor Decomposition, describes how a programmer can use the imprecision of a common machine learning algorithm, known as stochastic gradient descent, to his advantage. Normally this algorithm is used to speed up a slow learning process by only approximating the correct solution rather than working harder to get precision; however, Ge and his colleagues found that the small amount of “noise” created by the…