Firstly, we thank the reviewers for their valuable comments. Whilst it is not reasonable in practice to assume that data is sampled i.i.d. As previously stated, we believe our work forms a first step in achieving this goal. We believe that our theoretical model captures this dynamic. An insurance company may gather information from a customer to better evaluate potential risk.

We thank the reviewers for constructive feedback. "Importantly, the number of components is actually decided by the quality of the augmentation operation: an ideal The accuracies of different algorithms are shown in () . Effectively, such a graph has more edges and better connectivity. We will include this detailed explanation in the future version. Please see Figure 1 for an illustration.

We thank all three reviewers for their very thoughtful and detailed comments, with which we largely agree. We will include a detailed example in any revised version. The critique of untested influences refers to potential "unknown unknowns" in the data-27 This is generally not possible in a synthetic system. This is 2599 for networking, 473 for jdk, and 5000 for postgres. We acknowledge there may be issues with low sample sizes and will discuss this in any revised version.

We would like to thank our reviewers for their constructive comments. R1: Not first to look at full meshes. Accordingly, we will limit our claim to deep learning approaches for human pose reconstruction. We will release those, along with pre-trained models. Why does fig 4 show stick figures?