Review for NeurIPS paper: A convex optimization formulation for multivariate regression

This paper proposes a new parametrization of the multivariate linear regression problem. It shows that under this new parametrization, it is easier to employ sparsity inducing penalty terms on the inverse covariance matrix. The paper suggests a sequential relaxation algorithm. The reviewers noted the novelty of the approach and numerous strengths. The simulation experiments (in the supplementary material) explore the method in the context of several connectivity scenarios. However, one weakness is the exploration of the performance of the model on real data scenarios.