Call for Papers

We invite submissions to the Mathematical and Scientific Machine Learning.

MSML2021 is the second edition of a newly established conference, with emphasis on promoting the study of mathematical theory and algorithms of machine learning, as well as applications of machine learning in scientific computing and engineering disciplines. This conference aims to bring together the communities of machine learning, applied mathematics, and computational science and engineering, to exchange ideas and progress in this fast growing field.

Hybrid track: main conference and workshop contributions

MSML21 will be a hybrid venue: On the one hand, the main conference track will feature contributed talks and poster presentations selected from the original submissions. On the other hand, the workshop track will feature invited works from selected topics. Stay tuned for further announcements regarding contributions to the workshop track; please contact

Reviewing Procedure

In an effort to blend the advantages between conference and journal publication models, MSML21 will run a hybrid reviewing procedure consisting on two rounds of review plus a desk-reject phase.

Upon submission, the Program Committee will assess papers out of the scope of the conference as well as submissions clearly below the acceptance bar. After the initial review, the authors will have four weeks to submit an updated revision, as well as an author response summarizing the changes. These changes will be reviewed again, leading to the final decision made by the Program Committee. The PC reserves the right to solicit additional reviews.

Key dates

  • Submission deadline: December 4th, 2020, 11:59pm (AOE) December 11th, 11:59pm (AOE).
  • First round reviews: February 1st, 2021.
  • Revision deadline: February 28th, 2021.
  • Final Decisions : March 20, 2021.
  • Conference: August 16-19th, 2021.

Submission Instructions

Papers should be submitted by  Dec 11, 2020 11:59 PM AOE using the conference submission system at:

Style files and Templates

To prepare your submission to MSML 2021, please use the style files provided here.

  • The page limit of the paper submission is 15 JMLR-style single column pages, with unlimited additional pages for citations.
  • Authors may have include appendices (after the bibliography) to provide further detail, while the total page count of the appendices may not exceed 20, to make the review process manageable.
  • Do not include author names (this is done automatically), and to the extent possible, avoid directly identifying the authors. You should still include all relevant references, including your own, and any other relevant discussion, even if this might allow a reviewer to infer the author identities.
  • There will be an optional field to submit link to code at the time of submission. Code submission can strengthen the review process and we encourage all authors to submit code as part of their submission. Please keep in mind that code submitted should not reveal the identities of authors.

Issue with Inserting Graphics: If you used a previous style version, you may have encountered an issue when trying to add a figure. Please paste these three lines before the \begin{document}:


(thanks to Raphael Larsen for raising this issue!)

Dual Submission Policy

Submissions 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 or journals, are not allowed and violate our dual submission policy. However, papers that cite previous related work by the authors and papers that have appeared on non-peered reviewed websites (like arXiv) or that have been presented at workshops (i.e., venues that do not have publication proceedings) do not violate the policy. The policy is enforced during the whole reviewing process period. Submission of the paper to archival repositories such as arXiv is allowed.


The accepted papers of this conference will be published in the series of Proceedings of Machine Learning Research.