ICML 2019 : 36th International Conference on Machine Learning
Conference Series : International Conference on Machine Learning
Call For Papers
ICML 2019 Call for Papers
The 36th International Conference on Machine Learning (ICML 2019) will be held in Long Beach, CA, USA from June 10th to June 15th, 2019. The conference will consist of one day of tutorials (June 10), followed by three days of main conference sessions (June 11-13), followed by two days of workshops (June 14-15). We invite submissions of papers on all topics related to machine learning for the conference proceedings, and proposals for tutorials and workshops.
This year, ICML will adopt a single reviewing cycle, with an abstract submission deadline of January 18th, 2019 15:59 pacific, 23:59 Universal time and a full paper submission deadline of January 23th, 2019 15:59 pacific
Submissions will open on January 7th, 2019 12:00 pacific time and are managed through CMT:
Authors should include a full title for their paper, as well as a complete abstract by the abstract submission deadline. Submissions that have “placeholder” (test, xyz, etc.) titles or abstracts (or none at all) at the abstract submission deadline will be deleted. Authors of these types of submissions will not be allowed to submit a full paper on January 23, 2019.
Submitted papers can be up to eight pages long, not including references, and up to twelve pages when references and acknowledgments are included. Any paper exceeding this length will automatically be rejected. Authors have the option of submitting one supplementary manuscript containing further details of their work and a separate file containing code that supports experimental findings; it is entirely up to the reviewers to decide whether they wish to consult this additional material.
To foster reproducibility, we highly encourage authors to submit code. Reproducibility of results and easy availability of code will be taken into account in the decision-making process.
All submissions must be electronic, anonymized and must closely follow the formatting guidelines in the templates; otherwise they will automatically be rejected. This year, the author list at the submission deadline will be considered final, and no changes in authorship will be permitted for accepted papers.
Dual Submission Policy
It is not appropriate to submit papers that are identical (or substantially similar) to versions that have been previously published, or accepted for publication, or that have been submitted in parallel to other conferences. Such submissions violate our dual submission policy, and the organizers have the right to reject such submissions, and remove them from the proceedings.
There are several exceptions to this rule:
Submission is permitted of a short version of a paper that has been submitted to a journal, but will not be published in that journal on or before June 2019. Authors must declare such dual-submissions either through the CMT submission form, or via email to the program chairs (firstname.lastname@example.org). It is the author’s responsibility to make sure that the journal in question allows dual concurrent submissions to conferences.
Submission is permitted for papers presented or to be presented at conferences or workshops without proceedings (e.g., ICML or NIPS workshops), or with only abstracts published.
Submission is permitted for papers that are available as a technical report (or similar, e.g., in arXiv). In this case we suggest the authors not cite the report, so as to preserve anonymity.
Finally, note that previously published papers with substantial overlap written by the authors must be cited in such a way so as to preserve author anonymity. Differences relative to these earlier papers must be explained in the text of the submission. For example, (This work develops [our earlier work], which showed that).
Accepted papers must contain significant novel results. Results can be either theoretical or empirical. Results will be judged on the degree to which they have been objectively established and/or their potential for scientific and technological impact. Reproducibility of results and easy availability of code will be taken into account in the decision-making process.
Style and Author Instructions (coming soon)
Kamalika Chaudhuri (University of California, San Diego)
Ruslan Salakhutdinov (Carnegie Mellon University)
Eric Xing (Carnegie Mellon University)