b5b8c484824d8a06f4f3d570bc420313-AuthorFeedback.pdf

We thank all the reviewers for the valuable comments. Advantages of CSGLD over M-SGD: (i) CSGLD belongs to the class of adaptive biasing force algorithms and Empirically, we suggest to partition the sample space into a moderate number of subregions, e.g. Drawbacks of simulated annealing (SA) and replica exchange SGLD (reSGLD)/parallel tempering: SA can only be Q2. Missing baselines: We further compared CSGLD with CyclicalSGLD and reSGLD on an asymmetric mixture We will include the baselines and references in the next version. The gradient-vanishing problem in SGLD is not clear: Please refer to our reply to Q1 of Reviewer 1. Q1. Comments on bizarre peaks: A bizarre peak always indicates that there is a local minimum of the same energy in Q3.