1. Figure 2 shows that, without employing data augmentation and similarity-based regularization, the performance of CR-OSRS is comparable to RS-GM.
2. Could acceleration be achieved by incorporating entropy regularization into the optimization process?
3. It would be beneficial if the authors could provide an analysis of the computational complexity of this method.
4. The author wants to express too much content in the article, resulting in insufficient details and incomplete content in the main text.
5. The experimental part needs to be reorganized and further improved.
Details comments
1) It is recommended to swap the positions of Sections 4.3 and 4.4. According to the diagram, 4.3 is the training section, and 4.4 aims to measure certified space. Both 4.1 and 4.2 belong to the robustness and testing sections. Therefore, putting these parts together feels more reasonable.
2) The author should emphasize "The article is a general and robust method that can be applied to various GM methods, and we only use NGMv2 as an example." at the beginning of the article, rather than just showing in the title of Method Figure 1. This can better highlight the characteristics and contribution of the method.
3) The experimental part needs to be reorganized and further improved. The experimental section has a lot of content, but the experimental content listed in the main text does not highlight the superiority of the method well, so it needs to be reorganized. Based on the characteristics of the article, the experimental suggestions in the main text should include the following: 1. Robustness comparison and accuracy analysis with other empirical robustness algorithms for the same type of perturbations, rather than just focusing on the RS method, to clarify the superiority of the method. (You should supplement this part.) 2. Suggest using ablation experiments as the second part to demonstrate the effectiveness of the method. 3. Parameter analysis, elucidating the method's dependence on parameters. 4. Consider its applications on six basic algorithms as an extension part. Afterwards, based on the importance, select the important ones to place in the main text, and show the rest in the appendix.
4) In P16, the proof of claim 2, it should be P(I \in B) not P(I \in A).
5) In Table 2 of appendix, the Summary of main existing literature in learning GM can list the related types of perturbations.
6) In Formula 8, please clarify the meaning of lower p (lower bound of unilateral confidence), and the reason and meaning of setting as 1/2.
3) The experimental part needs to be reorganized and further improved. The experimental section has a lot of content, but the experimental content listed in the main text does not highlight the superiority of the method well, so it needs to be reorganized. Based on the characteristics of the article, the experimental suggestions in the main text should include the following: