Review for NeurIPS paper: Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian

The problem itself involves an extremely wide interest group across all areas of ML/AI. The outcome and the insights from this work can be applied to any problem in AI where non-convex optimization is involved. For instance Theorem 1, provides a very important theoretical result that is essential towards realizing why the approach might work. 5. The approximate version of the RR algorithm is also a a very nice addition, given that for larger sample spaces eigen decomposition of Hessian computation can be prohibitive and thus updates using Hessian-eigenvector products is a very nice touch.