Review for NeurIPS paper: A Catalyst Framework for Minimax Optimization

Additional Feedback: Questions in random ordering: - Would it be possible to provide dependences on the diameter(s) D_Y (and D_X?) in Table 1? - Reference for point (ii) page 3? - line 147: although this additional evaluation is certainly "negligible" for deterministic methods, is it really the case for stochastic ones? Was this cost taken into account in the numerical experiments? I guess there should be no gain (due to lower bound & EG), but e.g., do we also lose the logarithmic factor? If not, please make it more explicit (e.g., in the abstract; "state-of-the-art" makes it a bit implicit) To go further: - Is it possible to use the method with raw estimates of mu and/or l? - (lines 42-54): Given that there is no known optimal algorithm; is it possible that the lower bound is not tight? In particular, in the abstract, the word "first" is probably a bit abusive, given that there exists closely related methods for closely related settings (e.g., [40]).