Review for NeurIPS paper: Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining

Weaknesses: Cons: In general I found the method section ok, however some important parts are missing and need to be addressed. "Fit objective model h" (pseudo algo line 6) What is h and how is it fitted. You mention a gaussian process for the Zinc dataset - why is that model appropriate and how well does it actually fit the true objective function? "suggest new latent z based on h" (pseudo algo line 6) How do you find new latent space samples? Some of this information can likely be found in the refs or in the appendix however this information (in my opinion) really needs to be explained and self-contained in the main paper It would strengthen the paper a lot if one more real world example were included in the experimental results (currently two toy tasks, one real world dataset).