Important dates
Submission due: June 15 Extension: June 17, 2024 at 11:59pm PST.
Decision notification: July 30, 2024
Meeting dates: September 5 - 6, 2024
Poster and oral presentation instructions
Oral presentations: TBD
Poster presentations: TBD
Submission instructions
Papers should describe research on novel learning approaches or application areas in computational biology. A strong submission to the workshop typically presents a new learning method that yields new biological insights or applies an existing learning method to a new biological problem. However, submissions that improve upon existing methods for solving previously studied problems will also be considered. Examples of research presented in previous years: MLCB2023, MLCB2022, MLCB2021, MLCB2019.
We will accept submission in two tracks through CMT: https://cmt3.research.microsoft.com/MLCB2024/.
1) 8-page papers that are eligible to (optionally) be published online in an MLCB section of the Proceedings of Machine Learning Research.
2) 2-page abstracts which is more appropriate for work in progress (not eligible for publication).
Format and submission guidelines
Researchers should upload a 2 page abstract (excluding references) or an 8 page paper (excluding references) in PDF format to the MLCB submission website. Submissions exceeding the page limit will be automatically rejected.
No special style is required for 2-page abstracts. For 8-page papers, we ask authors to use standard font size 11 pt and margins 1 in. We prefer single-column format.
We also allow parallel submission to the preprint servers, and the inclusion of appendix/supplementary materials as long as the main paper is self-contained and reviewers are not required to read the appendix.
The workshop allows submissions of papers (for both tracks) that are under review or have been recently published in a conference or a journal. If you are submitting such a paper, please specify whether the submission is published or under consideration. Such submissions are not eligible for publication in PMLR.
We are no longer asking the authors to anonymize their submissions, i.e. reviewing is single blind not double blind.