Reviews: Distributionally Robust Graphical Models

Distributionally Robust Graphical Models The authors suggest dealing with the structure prediction task using adversarial graphical model (AGM), a generative model trained using an adversary distribution, instead of the empirical one. Instead of focusing on the loss metric, the authors focus on the graphical model allowing more flexibility wrt the loss metric. The AGM algorithm has similar complexity to conditional random fields but is less limited in its loss metric and is Fisher consistent for additive loss metrics. Following a complex mathematical transformation, the authors provide optimization of node and edge distribution of the graphical model but since this optimization is intractable, they restrict their method to tree-structural models or models with low treewidths. I would expect the authors to discuss this limitation of their algorithm.