JMLR Information for Authors
- Submission procedure (submit at http://jmlr.csail.mit.edu/manudb)
- Publication fees
- Discussion Articles
- Open Source Software
- Links - authors guide, forms and style files
- Final paper preparation
ScopeJMLR seeks previously unpublished papers on machine learning that contain:
- new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature;
- experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems;
- accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods;
- formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks;
- development of new analytical frameworks that advance theoretical studies of practical learning methods;
- computational models of data from natural learning systems at the behavioral or neural level
All claims should be clearly articulated and supported either by empirical experiments or theoretical analyses. When appropriate, authors are encouraged to implement their work and to demonstrate its utility on significant problems; any experiments reported should be reproducible. Papers describing systems should clearly describe the contributions or the principles underlying the system. Papers describing theoretical results should also discuss their practical utility. In general, it should be clear how the work advances the current state of understanding and why the advance matters. Papers should report on what was learned in doing the work, rather than merely on what was done.
Authors must clearly acknowledge the contributions of their predecessors. If a paper introduces new terminology or techniques, it should also explain why current terminology or techniques are insufficient.
Submissions to JMLR cannot have been published previously in any other journal. We will consider submissions that have been published at workshops or conferences. In these cases, we expect the JMLR submission to cite the prior work, go into much greater depth and to extend the published results in a substantive way. In all cases, authors must notify JMLR about previous publication at the time of submission, and explain the differences from their prior work.
We will also consider concurrent submissions of papers that are under review at conferences, provided the conference explicitly allows for this. In this case, too, we expect that the difference between the papers satisfy the above requirements, and we ask authors to provide their conference submission to the JMLR action editor in charge of the JMLR submission.
Examples of (possibly) acceptable 'deltas' beyond a conference paper include: new theoretical results, entirely new application domains, significant new insights and/or analyses. Examples of insufficient deltas include: adding proofs that were omitted from a conference paper; minor variations or extensions of previous experiments; adding extra background material or references. However, we ultimately leave the decision about whether a 'delta' is significant enough up to the individual reviewers.
Authors may submit work to JMLR that is already available as a preprint, for example on arXiV or personal websites.
JMLR accepts submissions via its own electronic submission management system.
Submissions must be typeset in LaTex using the JMLR LaTeX style file described in the authors guide. Only PDF files should be submitted. Papers not in the JMLR style file will be rejected without review
Articles may be accompanied by online appendices containing data, demonstrations, instructions for obtaining source code, or the source code itself if appropriate. We strongly encourage authors to include such appendices along with their papers. (Note: if an online appendix contains source code, we will require you to sign a release form prior to publication freeing us from liability.) We also strongly encourage authors to submit their data sets to the UCI Machine Learning Repository.
Articles must be accompanied by a cover letter (in PDF or plain text format) containing all of the below:
- Disclosure of any previous publications by the author(s) that significantly overlap with the submission. E.g., if the submission is an extended version of an earlier conference paper, please described the differences between the two papers. Simultaneous submission of papers to JMLR and other venues is not allowed.
- Confirmation that all co-authors are aware of the current submission and consent to its review by JMLR.
- Declaration of possible conflicts of interest; in particular, name all action editors that have recently collaborated with authors of the submission.
- Suggestions of 3 to 5 action editors that seem best suited to handle the submitted manuscript. These suggestions will be considered (though not necessarily honored). We have found that some action editors are popular with many authors, and may be busy handling other papers at a given point in time, so listing at least three action editors helps us expedite the process.
- Suggestions of 3 to 5 reviewers. These suggestions will be considered (though, again, not necessarily taken) by the action editor. As a reminder, do not suggest reviewers for whom there is a conflict of interest. As one example, an advisee / advisor relationship is a lifelong conflict of interest.
To submit a paper, please
- Prepare it in PDF format (if the submission contains multiple files, please create an archive in tar or zip format).
- Prepare the cover letter as described above in another PDF or plain text file.
- Ensure that the file to be uploaded is less than 5Mb in size.
- Ensure that the title page contains
- complete name, post and e-mail address of the corresponding author;
- a condensed running title of fifty (50) characters or less
- a list of five key-words
- an abstract that does not exceed 200 words
- Go to http://jmlr.csail.mit.edu/manudb, register and log in.
- Select the "submit manuscript" link across the top and upload your manuscript into the system.
You can then confirm that your manuscript has been received by selecting (at the top of the page) "manuscript listing" followed by "author." Your manuscript's ID tag should appear along the left-hand side. Following its link will give the current status of the manuscript.
We recommend authors look at Joelle Pineau's useful checklist about reproducibility in machine learning.
JMLR has a commitment to a rigorous review process, outlined below. Due to an ever increasing number of submissions, our timeline is as follows. After initially submitting a paper, an action editor will be assigned within about one month. The AE will then pick 3 reviewers who may take 1-3 months to review your paper. The AE makes the final decision, which will usually be available within 4-6 months after initial submission. However, some papers may take longer (e.g., if they are especially long or complex, or are received during the review period of a major conference, or if all suitable action editors are overloaded), and a few papers may take less time (e.g., if they are rejected without review). The detailed steps are outlined below.
The JMLR Paper Review Process
- When a paper is submitted to JMLR, it is scanned by the Editor-in-Chief (EIC). If the EIC finds that the paper is very clearly below the standards of the journal, or not in its scope, of if there are no suitable action editors, then the paper can be rejected without written review.
- The EIC assigns the paper to an action editor (AE) who has expertise in the area of the paper. If the AE finds that the paper is very likely to be rejected on full review, the AE will write a single short review explaining that position, and the paper will be rejected.
- The AE assigns the paper to three reviewers. The reviewers write detailed technical reviews of the paper.
- If only one review arrives in reasonable time, and that review is detailed and of high quality, and recommends rejection, and the AE agrees with the review, then the AE may decide to reject the paper based on the single review. If two reviews come in, and the decision is clear to the AE, the paper may be accepted or rejected. Or, the AE may decide to wait for the third review or even comission additional reviews, until the decision is clear.
- Possible decisions are Accept and Reject. Accepted papers may still require minor revisions, but those are usually checked only by the action editor. Rejection may or may not be accompanied by the encouragement to resubmit. Papers that are rejected will fall into two categories: those that are permitted to be resubmitted and those that are not. Authors should obtain explicit permission from an action editor before resubmitting a paper; to avoid misunderstandings, please contact the action editor or editor-in-chief if there is any ambiguity in the decision-making progress. Permission for resubmission should not be interpreted as any sort of guarantee of acceptance upon resubmission.
A quarterly paper volume will be published and sold to libraries and individuals.
Papers that fall in the area of support vector machines and kernel methods will be considered for joint publication in JMLR and on www.kernel-machines.org. If appropriate, please indicate in your cover letter that you would like your submission to be considered for inclusion in the kernel section. Further information can be found in the JMLR section of the kernel machines web repository.
There are no publication fees associated with this journal and all papers are freely available to readers.
- Authors guide: http://www.jmlr.org/format/authors-guide.html
- Reviewers guide: http://www.jmlr.org/reviewer-guide.html
Instructions for Formatting JMLR Articles: http://www.jmlr.org/format/format.html, also in
- Common formatting errors: http://www.jmlr.org/format/formatting-errors.html
- Annotated sample JMLR paper and style file
Permission to publish form: in PDF format.
- Software release form: PDF format. To be sent along with the permission to publish form if any executable code is made available as part of an online appendix.
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