The Action Editors (AEs) have a central importance in ensuring the accepted papers are up to standards of best practices in data-centered machine learning research. Briefly, AEs are responsible for prescreening assigned submissions, recruiting reviewers for submissions that pass prescreening, ensuring that all submissions receive quality reviews, facilitating discussions among reviewers, evaluating the quality of reviews, making decision recommendations, and making sure the camera-ready papers are ready to be published.Below are your detailed duties as an Action Editor (AE). Before you proceed, please review the Reviewer Guidelines, Acceptance Criteria and Code of Conduct.
Pre-screeningWithin a week after your assignment as an Action Editor (AE), your duties begin by evaluating whether the submission successfully clears the pre-screening stage. If the submission breaches the DMLR LaTeX style guidelines and/or the NeurIPS Code of Ethics or falls out of alignment with DMLR's scope, and if it clearly fails to meet DMLR's journal standards, it should be desk-rejected. In cases where you have uncertainties, please do not hesitate to seek guidance from the Editors-in-Chief.
If the submission successfully passes the pre-screening, you may confirm this via your OpenReview console. Subsequently, the submission will be made accessible to the public, and your identity as the responsible AE will be disclosed.
Assignment of ReviewersAfter confirming a submission's suitability for the DMLR Journal, you should proceed to assign three reviewers to review the submission. In order to ensure a seamless review process, we recommend that you initially reach out to reviewers whose judgment you have confidence in. However, if necessary, we can provide you with the contact information of individuals who expressed interest in reviewing for DMLR.
Members of the general public may also express their interest in reviewing papers. The decision to accept such offers is at the discretion of the AE.For papers that are shorter than 25 pages (excluding the Appendix and supplementary documents), the expected review period is one month. For longer papers, the Action Editor (AE) may determine an appropriate review duration. In both cases, DMLR prioritizes the quality of reviews over speediness. As such, the decision on whether the reviews meet the required quality standards rests with the AE.
Post-Review DiscussionOnce all the reviews have been submitted, they will become visible to other reviewers, and you can initiate the post-review discussion phase. During this period, it is expected that the reviewers will actively engage in discussions and collaborate to reach a consensus regarding their recommendation, whether the submission aligns with the Acceptance Criteria or not. Additionally, this phase allows for the possibility of directing specific questions to the authors.
Should you have concerns regarding the ethical implications of a submission, you have the option to flag it for Ethical Review on OpenReview, in accordance with the NeurIPS Code of Ethics that we closely adhere to. Upon flagging a submission, the Editors-in-Chief will be notified, and an Ethics Reviewer will be assigned to assess the submission in terms of ethical considerations.
DecisionThe discussion phase concludes when you make a final decision for the paper, which can be either an acceptance or a rejection. In the event of acceptance, you have the option to request minor revisions necessary for the camera-ready submission. However, if the decision is a rejection, the authors will be promptly notified of this outcome.
Camera-Ready SubmissionUpon acceptance, authors are required to provide a "camera-ready" version incorporating the requested revisions if applicable. This camera-ready version must adhere to the DMLR LaTeX style guidelines and include a link to the paper's review page. Just as in the initial submissions, DMLR encourages authors to submit any supplementary materials that enhance and support their paper, such as datasets, appendices, (audio)visual presentations, or code.
AcknowledgmentsThe above guidelines are adapted from that of TMLR as well as NeurIPS Datasets and Benchmark Track.
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