Review for NeurIPS paper: Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function

Additional Feedback: The main contribution of this paper is a single-loop stochastic algorithm which achieves the best-known complexity bound and outperforms the existing prox-linear algorithms in computational sense. To make this argument more convincing, I hope the authors can address the following concerns: 1. Clearly state why your algorithm outperforms prox-linear algorithms. Indeed, by simply exploring the structure of stochastic problem in Eq.(1), the prox-linear subproblem can be reformulated using conjugate function and becomes the same as your subproblem. Indeed, when b and \psi are in special forms, I agree that we can achieve the closed-form solution as you point out. However, this also holds true for prox-linear algorithms and a few other double-loop algorithms.