Review for NeurIPS paper: A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons

Additional Feedback: - The authors claim that empirically they do not need large amounts of repeated stimuli for the method to work. This empirical claim is based on only a single experimental dataset. It would be nice to see some theoretical analysis or exploration into how much data is needed for this to work -- presumably if my data has only 2 repeats of a stimulus then the h_stim auxilliary variable could be very poorly estimated. This introduces a bias into the results of the model, but how bad is this bias? Is this correction procedure provably optimal in some way?