Weakness: Originality:
1.I want to know if this paper is the first time to study the problem of the robust collaborative inference, where there are both arbitrary agents and adversarial agents. The arbitrary agents are easy to identify. However, I’m afraid the proposed method achieves a similar performance to identify the adversarial agents compared with baselines.
From Eq.(5), the framework aims to find a combined feature l
which is on the manifold and is near h
. The manifold projection could get a similar results for the adversarial sub-features. Could the authors discuss more about it? Writtings:
1.After so many times of reading, I guess I understand this paper. The authors introduce their method in Section 2.3, which is very simple. However, it relies block-sparse structure which is detailed stated in Section4. This could cause confuse when understanding the proposed method.
2.The notations are confusing. For example, h and l
both denote the feature. Why not use a letter (or with its variants)?
the citation format may be ICLR rather NeurIPS.
Theoretical analysis:
1.This paper provides an extensive theoretical analysis. In fact, I suggest the authors discuss more what the analysis means. Compared with baselines, why CoPur could do better.
2.Could the authors give an intuitive explanation about the effect of the sparsity α
on CoPur? Experiments:
1.From the ablation studies, CoPur achieves a better performance compared with the manifold projection, what if there are different Ω c
and different Ω a d v ?
2.More analysis is helpful, for example, The comparison on optimization efficiency.
The authors discuss the limitations in Appendix. I have no other suggestions.
2.The notations are confusing. For example, h and l both denote the feature. Why not use a letter (or with its variants)? the citation format may be ICLR rather NeurIPS. Theoretical analysis: