  Local search heuristics for non-convex optimizations are popular in applied
machine learning. However, in general it is hard to guarantee that such
algorithms even converge to a local minimum, due to the existence of
complicated saddle point structures in high dimensions. Many functions have
degenerate saddle points such that the first and second order derivatives
cannot distinguish them with local optima. In this paper we use higher order
derivatives to escape these saddle points: we design the first efficient
algorithm guaranteed to converge to a third order local optimum (while existing
techniques are at most second order). We also show that it is NP-hard to extend
this further to finding fourth order local optima.
