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ICML 2020 Review Form

To help ICML 2020 participants better understand the review process, we are making the reviewer form available here. Questions with an asterix are required. The form explicitly mentions who will see what answers. When the form says that an entire is "visible to other reviewers", this means that it is visible to other reviewers only after they have submitted their own reviews.

1. Please summarize the main claim(s) of this paper in two or three sentences. *   (visible to authors during feedback, visible to authors after notification, visible to other reviewers, visible to meta-reviewers)

 

2. Merits of the Paper. What would be the main benefits to the machine learning community if this paper were presented at the conference? Please list at least one. *   (visible to authors during feedback, visible to authors after notification, visible to other reviewers, visible to meta-reviewers)

 

3. Please provide an overall evaluation for this submission. *   (visible to authors during feedback, visible to authors after notification, visible to other reviewers, visible to meta-reviewers)

  • Outstanding paper, I would fight for it to be accepted.
  • Very good paper, I would like to see it accepted.
  • Borderline paper, but has merits that outweigh flaws.
  • Borderline paper, but the flaws may outweigh the merits.
  • Below the acceptance threshold, I would rather not see it at the conference.
  • Wrong or known results, I would fight to have it rejected.
     

4. Score Justification. * Beyond what you've written above as "merits", what were the major considerations that led you to your overall score for this paper?   (visible to authors during feedback, visible to authors after notification, visible to other reviewers, visible to meta-reviewers)

 

5. Detailed Comments for Authors. * Please comment on the following, as relevant:
- The significance and novelty of the paper's contributions.
- The paper's potential impact on the field of machine learning.
- The degree to which the paper substantiates its main claims.
- Constructive criticism and feedback that could help improve the work or its presentation.
- The degree to which the results in the paper are reproducible.
- Missing references, presentation suggestions, and typos or grammar improvements.   (visible to authors during feedback, visible to authors after notification, visible to other reviewers, visible to meta-reviewers)

 

6. Please rate your expertise on the topic of this submission, picking the closest match. *   (visible to authors during feedback, visible to authors after notification, visible to other reviewers, visible to meta-reviewers)

  • I have published one or more papers in the narrow area of this submission.
  • I have closely read papers on this topic, and written papers in the broad area of this submission.
  • I have seen talks or skimmed a few papers on this topic, and have not published in this area.
  • I have little background in the area of this submission.

 

7. Please rate your confidence in your evaluation of this paper, picking the closest match. *   (visible to authors during feedback, visible to authors after notification, visible to other reviewers, visible to meta-reviewers)

  • I am very confident in my evaluation of the paper. I read the paper very carefully and I am very familiar with related work.
  • I tried to check the important points carefully. It is unlikely, though possible, that I missed something that could affect my ratings.
  • I am willing to defend my evaluation, but it is fairly likely that I missed some details, didn't understand some central points, or can't be sure about the novelty of the work.
  • Not my area, or the paper was hard for me to understand.

 

8. Datasets. If this paper introduces a new dataset, which of the following norms are addressed? (For ICML 2020, lack of adherence is not grounds for rejection and should not affect your score; however, we have encouraged authors to follow these suggestions.)   (visible to authors during feedback, visible to authors after notification, visible to other reviewers, visible to meta-reviewers)

  • This paper does not introduce a new dataset (skip the remainder of this question).
  • A link to the dataset is provided in the paper.
  • The dataset is deposited in a repository that ensures long term preservation of the data.
  • The dataset has a persistent identifier such as Digital Object Identifier or Compact Identifier.
  • The dataset adheres to Schema.org or DCAT metadata standards.
  • The license and/or any data access restrictions are described in the paper.
  • The paper includes a convincing justification of the special nature of the dataset that makes it impossible to conform to these suggestions.

 

9. Confidential Comments to Meta-Reviewer. Use this section to write any comments that only meta-reviewers ("area chairs") will see. Please use this section sparingly, in particular for things that may break anonymity of yourself or of the authors (to other reviewers).   (visible to meta-reviewers)

 

10. Creative Paper? We wish to identify particularly creative papers that study a new problem or involve a very novel idea or insight. Please check this box if you feel that this submission is creative enough to be accepted even if there are some weaknesses in execution.   (visible to meta-reviewers)

 

11. Social/Humanitarian Relevance? We also wish to highlight papers that work on problems with strong social or humanitarian relevance. Please check this box if you feel that this submission is sufficiently socially relevant to be accepted even if there are some weaknesses in execution.   (visible to meta-reviewers)

 

12. I agree to keep the paper and supplementary materials (including code submissions and Latex source) confidential, and delete any submitted code at the end of the review cycle to comply with the confidentiality requirements. *   (visible to authors during feedback, visible to authors after notification, visible to other reviewers, visible to meta-reviewers)

 

13. I acknowledge that my review accords with the ICML code of conduct (see https://icml.cc/public/CodeOfConduct). *   (visible to authors during feedback, visible to authors after notification, visible to other reviewers, visible to meta-reviewers)

Email: program-committee@icml.cc

Program Chairs:
Hal Daumé III (University of Maryland, Microsoft Research)
Aarti Singh (Carnegie Mellon University)

General Chair:
David Blei (Columbia University)