Review for NeurIPS paper: Fair regression with Wasserstein barycenters

Weaknesses: My biggest worry is that I'm not sure whether this work adds significantly new contributions compared to the previous literature that uses optimal transport theory for fair classification. It seems like it's the modification of the post-processing approach in "Wasserstein Fair Classification" (Jiang et al). I would be happy to increase the score if the authors can highlight some challenges faced in updating approaches from previous work to this regression problem and how these challenges are not trivial. I wish there was a little more discussion about looking at these fairness constrained optimization problems through the lens of optimal transport theory; the paper only considered demographic parity, but maybe a discussion of why it is or is not immediate this approach may work for other fairness notions, such as equalized odds (appropriately're-defined' for the regression problem). Also, I wish whether it's possible to allow for some slack when considering demographic parity (difference can be at most some epsilon).