Review for NeurIPS paper: Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models

Weaknesses: I'm a bit confused about the evaluation of the approach. What is learned is a generative model over probabilistic graphical models; however, the focus in experiments I and II is on conditional MAP inference. In this setting, the model is being used as a structured output prediction model, and so comparisons are missing against other structured prediction models, examples being [1], [2], and [3] (see "related work" section for refs). If the primary use of this model is for conditional MAP inference, then it is important to understand how well AGM compares against other similar models. That being said, since the samples themselves are unconditional, this approach is at a disadvantage compared to these other approaches, which condition their "samples" on the input.