Review for NeurIPS paper: Prediction with Corrupted Expert Advice

Summary and Contributions: post-rebuttal update I am satisfied with the author's responses in their rebuttal and my score remains the same. In particular, regarding motivation for their own setting, I think the author's response about malicious users affecting the feedback of a learning algorithm is great. Regarding the simulations, the author's explained why some of the plots were weird. Rather than taking out these plots, I think it's great to leave them in and just explain the effect (as the author's did in their rebuttal). This paper studies decision-theoretic online learning (the Hedge setting), a full-information online learning game, under the assumption that the data is stochastic (with a gap in the mean between the best and second best experts) but only observed under adversarial corruption (with a parameter measuring the amount of corruption).