Our novelty has also been affirmed by R1, R2 and R4. However, we should clarify that (1) our work differs completely from MMD-GANs, and (2) although Ref [4] Our supplementary material includes the s.o.t.a. Below we discuss the reviewers' comments and will address all of them in the revision. Lipschitz constraint is not a necessity in our RCF-GAN. Please refer to our proof. Fig.4 in the paper shows the image reconstruction and interpolation, validating our superior performances on clear We will elaborate more upon this in the revision.

We would like to thank each of the reviewers for the constructive and insightful comments on our manuscript. Also, we will further polish our paper based on your suggestions to address other writing issues. The reasons are discussed in lines 308-315 in our paper. R3, R5: Explanation on why to use self-attention. In addition, we agree that it is more realistic to model label uncertainty.

We thank all the reviewers for their insightful comments, suggestions, and references. Novelty of tandem loss: it is not new, but we were not aware of the prior work, we thank Reviewer 2 for bringing it up. While most of the computed bounds are non-vacuous, they look to be not that tight. Also a discussion of potential ways to obtain tighter bond values, or whether there is a fundamental limitation. We provide some discussion in Sections 3.2 and 4.4.

Reviewer #1: We appreciate many insightful comments from this reviewer. We have included more scenarios in the paper. Here are three of them. In this paper, SM stands for the standard two-layer GCN model. In the last few days, we have tried very hard to carry out more experiments on other datasets including'Citeseer', and Table 1: Mean Prediction Accuracy for'Citeseer' Figure 1: Boxplot of RMSEs in real data analysis Reviewer #2: We appreciate many insightful comments from this reviewer.

We thank all reviewers for giving us the insightful comments. Then we collect all positive samples by a Breadth-First Search algorithm. We will add these results to our paper. We also give the qualitative analysis in Figure 2 (b). About the baseline, our baseline is the KNN method, i.e. directly using the nearst We have compared our algorithm with the KNN algorithm in Sec 4.2.

Duplicate annotations are rare, and will be corrected by the annotator in the review phase.

We thank the reviewers for their effort and insightful comments during these unprecedented times. LQR & LQG are among few continuous settings where the optimal policies exist (and mainly have closed form) [1]. Therefore, we do not see why this paper would be less relevant to our community. If PE is absent, we provide two general algorithms stated in Cor. The agent uses a warm-up period of O ( T) after which it commits to a controller yielding a regret of T .