Review for NeurIPS paper: Rescuing neural spike train models from bad MLE

Strengths: Updated Review: I'd like to thank the authors for answering my concerns re: the stochastic nature of the kernel optimization. I am now even more confident in my assessment that this is a good submission and an accept from me. In response to an under constrained general MLE framework for fitting autoregressive models, the authors introduce a method to balance multiple objectives (fidelity to outputs under data-constrained and free-running conditions). They achieve this with model-based MMD (sometimes with the addition of a likelihood objective), which requires matching of free-running and data-constrained model features, essentially maximizing likelihood under the constraint of requiring faithful behavior in both conditions. This leads to a model that is both faithful and stable in the free-running condition.