Review for NeurIPS paper: Markovian Score Climbing: Variational Inference with KL(p

Relation to Prior Work: Prior work is discussed but some important related work is missing. I listed some related work which in my opinion should be discussed below. Wang and colleges [1] develop a meta-learning approach to learn Gibbs block conditionals. The paper has a different focus and assumes to have access to samples from the true generative model but is still technically related. They optimize an inclusive KL-divergence and employ additional MH steps to maintain the correct stationary distribution.