Review for NeurIPS paper: Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms

Additional Feedback: Overall this paper is well presented and technically sound. However, I believe its technical contribution is minor and it does not have significant impact to this field. Thus I vote for a weak reject. To increase the contribution of this paper, the authors can consider designing training algorithms that improves the provable robustness of NPCs. For example, RSLVQ is a strong method (in Table 1 it achieves very competitive clean test error); can we improve its robustness to the same level of other baselines?