NeurIPS 2020

Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing

Meta Review

The paper studies principal component analysis for sub-Gaussian distributions. Two new methods for this problem are proposed, one of which uses width-independent Schatten packing SDPs. Reviewers agree that this is an interesting, non-trivial and solid theoretical work and should be accepted for NeurIPS. The rebuttal addressed the reviewers concerns adequately. The recommendation is to accept this paper for presentation at NeurIPS. We urge the authors to make the connection of the Schatten packing to the main approach more clearer in a final version of the paper.