Reviews: The promises and pitfalls of Stochastic Gradient Langevin Dynamics

Review after rebuttal: I thank the author(s) for their response. While I still believe that this paper is a minor increment beyond what has already been done on SGLD, I agree that the message might be useful for some. I also appreciate the effort the authors have made in improving the manuscript based on reviews' suggestions, particularly their efforts to include relevant numerical experiments to ML scenarios, and recommendations beyond the CV approach which has been studied to exhaustion and rarely applicable in practice. Based on this, I've adjusted my decision to marginally above threshold. Original review: In the paper "The promises and pitfalls of Stochastic Gradient Langevin Dynamics" the authors revisit the Stochastic Langevin Gradient Dynamics (SGLD) approach to approximately sampling from a probability distribution using stochastic gradients (specifically subsampling). The authors compare a number of different classes of approximate inference method, including SGLD, LMC (known by some as Unadjusted Langevin Algorithm or ULA) and Stochastic Gradient Langevin Dynamics Fixed Point (SGLDFP) -- the latter being a variant of SGLD with a control variate exploiting the unimodality of the distribution, similar to what has been presented in [3, 25 and others].