Review for NeurIPS paper: Learning under Model Misspecification: Applications to Variational and Ensemble methods

Summary and Contributions: POST REBUTTAL: I was happy to see that all important issues I raised were addressed to my satisfaction by the rebuttal. Having reviewed an earlier version of this paper for ICML, I know that the authors will keep the promises made in their rebuttal. One minor issue that the rebuttal addresses incorrectly are the assumptions on loss functions (as they relate to PAC-Bayesian bounds). The claim in the rebuttal that the results hold for'general unbounded losses' is incorrect. In particular, the probability's exponentially fast convergence towards zero is *not* guaranteed for general unbounded losses, and this is actually made rather clear in the references you provide [17,45].