原文来自:https://www.cnblogs.com/baidut/p/6375371.html
https://www.st.cs.uni-saarland.de/zeller/onresearch/rebuttal-patterns.php3
http://hyunyoungsong.wordpress.com/2010/12/18/how-to-write-a-acm-sigchi-rebuttal/
Note that the author rebuttal is optional, and serves to provide you with an opportunity to rebut factual errors in the reviews, or to supply additional information requested by the reviewers.
The rebuttal is limited to 4000 characters. Please be concise and polite. Comments that are not to the point or offensive will make rejection of your paper more likely. Make sure to preserve anonymity in your rebuttal. Links to websites that reveal the authors’identities are not allowed and will be considered a violation of the double-blind policy. Links to websites with new figures, tables, videos or other materials are not allowed.
撰写原则
阅读之前,你需要清楚以下几点:
rebuttal 只会在你论文处于接收边缘的时候起作用,如果reviewers意见普遍很严厉,那么rebuttal的作用几乎可以忽略。当然如果 reviewers 意见普遍很好且没有提问,也可以不写 rebuttal。
Rebuttal 是给审稿人和 area chair 看的。Confidential comment to Area Chair 一般用于举报审稿人(仅area chair可见),不用填。
说明: 带有星号*的部分为重点内容,其余为可选内容。
即使是反对你的审稿人,也拿出了很多宝贵的时间审阅你的论文,因此作为作者要学会感恩。
如果有几句话想让所有与会审稿人看到,那么这几句话一定要放在最前方。原因也很简单,每个人不可能把你的rebuttal全篇看完,但是前几句大家还是都会瞧几眼的。
We will re-structure the paper to improve clarity. We will also add more details (and add an example, space permitting) and clarify our contributions (Section 4.4) for better understanding. We will also fix minor typos.
Then we address major concerns below.
对于支持者的感谢
We thank the reviewer for the encouraging comments.
对于反对者的反驳*
一般而言,对于area chair,那个给分比较低的会自然吸引他的眼球,相对占得的权重也就大(这些是area chair的自己之谈),所以rebuttal就是要在有限的言辞里重点反驳这些reviewer。
小毛病可以承认
写作架构的批评认怂就行
R2: "The structure and writing is a concern"
We agree. This has been addressed in the arXiv version which has much cleaner structure and writing, including improved section on related work.
关于论述不清的回复
B. Apologies for being unclear in these parts of our paper, we address the individual points below, and will be more explicit on all of these in the full version.
小错误
\4. A list of minor typos.
Corrected.
关键问题必须反驳*
For Section 2.3, there may be some misunderstanding. The reviewer is correct that a simple alternative to our approach would be to run MAP on the latent variables, and then hold the latent variables S fixed and Bayesian melding method on the model variables when fixing the latent variables. However, this is computationally expensive and does not scale to high dimensions, as the previous Bayesian melding method requires performing density estimation for the distribution tau. Instead we propose an approximate joint prior in Section 3, which allows us to infer the latent variables and model parameters jointly. Thus our algorithm scales better than the original Bayesian melding algorithm.
关于缺失引用的回复
R4 Missing citations
Note that we do cite as [17] and discuss the work by Parks et al in L090-100. We further clarify the distinctions below. We will include the work by Demirkus et al in the next version and discuss head pose estimation below.
Thank you for the references, which are now included in the current draft.
然而并没有拉回这一票,显然这个评委(Reviewer_7)意见很大 nips28/reviews/1985.html
Thanks for pointing out some valuable related work. The first two works do not consider any feature and instead consider the noise that occurs in observations. The third work is more application oriented using metric learning. Although we also demonstrate our model on a similar application - semi-supervised clustering, our work aims to provide a more general treatment to noisy features on matrix completion. In addition, their "uncertain side information" in fact corresponds to similar/dissimilar information between items in semi-supervised clustering, which means the uncertainty they consider is also on observations, while the noise we consider is on features. We are happy to include these related work in our final submission.
To Reviewer 8
\1. This paper lacks the references for some related recent works (e.g. [1, Nesterov 2015] and [2, Lan 2014]).
We have included Lan's conditional gradient sliding paper in the reference [14], which we believe is more relevant than [2].
We will include [1] in the final version.
\3. We thank the reviewer for mentioning the papers of Burer and Monteiro (2005) and Lee and Bresler (2009). Both papers are certainly relevant related work, and should be discussed. The Burer and Monteiro (B&M) paper (with which we were previously familiar but neglected to cite) is important, and gives a helpful traceback of the factorization and nonconvex optimization idea in the optimization literature. While related, our algorithm and analysis are substantially different than these works. Essentially, B&M target the general semidefinite programming problem and have a more complex set of first order techniques for nonconvex optimization (BFGS and augmented Lagrangian techniques, etc.) It would not be easy to do a direct numerical comparison; but we would expect our methods to perform comparably. In contrast, our method is clean and simple, targets a more limited class of problems, and correspondingly allows us to obtain a strong theoretical convergence analysis (the pesky extra factor of r notwithstanding). As stated by Burer and Monteiro (2003) "Although we are able to derive some amount of theoretical justification for [global convergence], our belief that the method is not strongly affected by the inherent nonconvexity [of the objective function] is largely experimental." We hope that our work will contribute to and help spur the further development of this important class of techniques.
We apologize for not clarifying all questions given the limited space and many reviews. We will fix all typos and add missing references in the next revision.
We will address all remaining minor suggestions in the final revision
We will thoroughly check and fix grammatical errors in the final submission.
以下为笔者撰写本文时参考的资料,感兴趣的可以继续阅读。