We thank the reviewers for their feedback and reply to the major points raised by each reviewer individually. Our paper focuses on ImageNet classification because this is what almost all prior work on robustness has studied. We hope that future work (e.g., transfer learning research) can build on our testbed. Our results are substantially more nuanced than "more data helps": (i) We show that only more data currently helps This is a strong negative result. Appendix D contains additional results for more granular trends.

We thank all reviewers for their thoughtful feedback that can help enhance the presentation of our results. We will clarify this decision (as the reviewer recommends). P AC bound by taking the resulting mixture policy. We will add a note in the final version. The knapsack solver is provided in Appendix A.3 and is a linear program with We will discuss the additional challenges that arise in these settings and explicitly state them as future directions.

Among those presenting at last week's World Economic Forum in Davos, Switzerland, was Mary "Missy" Cummings, a Duke professor in the Department of Mechanical Engineering and Materials Science whose areas of expertise include artificial intelligence. Duke Today asked Cummings, who has presented before at the forum, about this year's event. Q: How, if at all, was the mood at Davos different this year than in previous years in which you have attended? CUMMINGS: Interestingly I did not see any real difference in the overall mood over last year, but (President) Trump's presence certainly created a buzz that was not as palpable as last year. Q: What were the 3 major points that you made during your presentation this year?