Review for NeurIPS paper: A Fair Classifier Using Kernel Density Estimation

Additional Feedback: I think the overall idea is somewhat intriguing, and agree that the distinction between hard prediction and model-assigned soft probabilities is typically glossed over in the literature. But sections 3 and 4 seems somewhat disconnected to me, since this differentiability issues does not come across strongly in the experiments (instead the instability of baselines due to adversarial training is emphasized). I also think that laying out the extending to multi-class classification would help the paper a lot (to save space experimental details could be moved to the appendix) About the experiments, there are several improvements to be made. I have a concern about whether the baselines were substantially tuned, since there is no discussion of how hyperparameters such as learning rates were selected for the baselines. I also didn't understand the choice of a narrow MLP architecture (14 hidden units per layer) for the neural net methods.