Call for Submissions to the Conference on Systems and Machine Learning Foundation (MLSys)
Authors are encouraged to submit previously unpublished research at the intersection of computer systems and machine learning. The MLSys Program Committee will select papers based on a combination of novelty, quality, interest, and impact.
Topics of interest include, but are not limited to:
- Efficient model training, inference, and serving
- Distributed and parallel learning algorithms
- Privacy and security for ML applications
- Testing, debugging, and monitoring of ML applications
- Fairness, interpretability and explainability for ML applications
- Data preparation, feature selection, and feature extraction
- ML programming models and abstractions
- Programming languages for machine learning
- Visualization of data, models, and predictions
- Specialized hardware for machine learning
- Hardware-efficient ML methods
- Machine Learning for Systems
- Systems for Machine Learning
Reviewing process: All submissions will be double blind, though authors are allowed to post their paper on arXiv or other public forums. Key dates related to the reviewing process are given below:
- Paper submission deadline: Oct 10, 2020
- Author rebuttal period: The date Author Feedback
- Begins: Nov 26, 2020
- Ends: Dec 07, 2020
Decision notification: Jan 18, 2021
Dual submission policy: We will not accept any paper which, at the time of submission, is under review for another conference or has already been published. This policy also applies to papers that overlap substantially in technical content with conference papers under review or previously published. Authors may, however, submit to the MLSys substantially different versions of journal papers that are currently under review by a journal, but not yet accepted at the time of submission. After submission and during the review period, MLSys submissions must not be submitted to other conferences. However, authors may submit to non-archival venues, such as workshops without proceedings, or as technical reports (or similar, e.g., arXiv). In the case of workshops with proceedings, the authors should explain the differences in the full paper and email the workshop paper to the program chair. These latter submissions will be evaluated on the added novelty compared to their workshop version.
Proceedings: Accepted papers will be published in the form of online proceedings.
Submission format: To prepare your submission to MLSys 2021, please use the LaTeX style files provided at: MLSys2021style.tar.gz. Submitted papers will be in a 2-column format and can be up to 10 pages long, not including references. Each reference must explicitly list all authors of the paper. Authors may use as many pages of appendices as they wish, but reviewers are not required to read these. If you wish to include an appendix, please upload this separately from your main paper. You will be able to upload the appendix after submitting your main paper.
Artifacts: Our conference promotes reproducibility of experimental results and encourages code and data sharing to help the community quickly validate and compare alternative approaches. We invite authors of accepted papers MLSys 2021 to submit their supporting materials (code, data, models, experimental workflows, results) to the Artifact Evaluation process based on the ACM Artifact Review and Badging policy, a standard for systems conferences including CGO, PLDI, PPoPP and SuperComputing. This submission is voluntary and will not influence the final decision regarding the papers..
Conflicts of Interest: Authors must register all their conflicts on the paper submission site. Conflicts are needed to ensure appropriate assignment of reviewers. If a paper is found to have an undeclared conflict that causes a problem OR if a paper is found to declare false conflicts in order to abuse or “game” the review system, the paper may be rejected. Please review the definitions of Conflict of Interest.
Submissions that violate these instructions may not be reviewed, at the discretion of the Program Chair, to ensure a review process that is fair to all potential authors.
Here is the link to the CMT site: https://cmt3.research.microsoft.com/MLSys2021/