Review for NeurIPS paper: Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters

Weaknesses: - It is not very clear about the evaluation for outer iterations. Is the number of aggregated tasks affecting the convergence too? In MAML, the gradient for outer-loop is computed based on the inner-loop of several tasks. Particularly, the number of samples in the support set and the query set (on mini-ImageNet) may affect the error and the convergence. Fallah et al. [4] considers this number of samples in the inner-loop to their analysis.