Review for NeurIPS paper: Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder

Weaknesses: My main concern is on the lack of comparison with previous methods (input complexity adjusted score and the likelihood ratio method) on the corresponding models(e.g. Those methods were developed and tested with the corresponding generative models in the original paper and it seems unfair to only compare with their method on VAEs. Without this comparison, if a researcher wants to choose the SOTA OOD detection method for their own applications, it's hard to tell which method will most likely achieve the best performance if they have the freedom to choose their own generative models. This is the main drawback and the main reason for my rating. Furthermore, this leads to the general motivation of the paper. I really like the analysis on why prior likelihood-ratio based methods didn't work as well on VAEs, however, if all we care about is detecting OOD examples, why is it absolutely necessary to have a method that works well on VAEs?