Submission guidelines and editorial policies

Author guidelines

Reviewer guidelines

Action Editor guidelines

Ethics guidelines

Code of conduct

Editorial board


Frequently Asked Questions

Transactions on Machine Learning Research

Transactions on Machine Learning Research (TMLR) is a new venue for dissemination of machine learning research that is intended to complement JMLR while supporting the unmet needs of a growing ML community. TMLR emphasizes technical correctness over subjective significance, to ensure that we facilitate scientific discourse on topics that are deemed less significant by contemporaries but may be important in the future. TMLR caters to the shorter format manuscripts that are usually submitted to conferences, providing fast turnarounds and double blind reviewing. We employ a rolling submission process, shortened review period, flexible timelines, and variable manuscript length, to enable deep and sustained interactions among authors, reviewers, editors and readers. This leads to a high level of quality and rigor for every published article. TMLR does not accept submissions that have any overlap with previously published work. TMLR maximizes openness and transparency by hosting the review process on OpenReview.

For more information on TMLR, see the following presentation given at the NeurIPS 2021 Pre-Registration Workshop:



Acting and founding Editors-in-Chief of TMLR are Hugo Larochelle (Google, Mila), Raia Hadsell (DeepMind) and Kyunghyun Cho (Genentech, New York University), along with Managing Editor Fabian Pedregosa (Google). The goal of TMLR and the Editors-in-Chief is to support the evolving needs of the machine learning community. We welcome your feedback and comments via e-mail: tmlr@jmlr.org .


We use the reviewing and publication systems on OpenReview for openness and transparency. Stay tuned for submission instructions.

Advisory Board Members

TMLR’s founding advisory board consists of nine experts who have contributed to and extensive experiences in creating, maintaining and improving academic publication venues, conferences and workshops in both machine learning and adjacent areas.

Yoshua Bengio: Mila.

Andrew McCallum: University of Massachusetts, Amherst.

Shakir Mohamed: DeepMind.

Bernhard Schölkopf: Max Planck Institute for Intelligent Systems.

Natalie Schluter: IT University, Copenhagen.

Konrad Körding: University of Pennsylvania.

Lillian Lee: Cornell University.

Devi Parikh: Facebook and Georgia Tech.

Alexandra Chouldechova: Carnegie Mellon University.


TMLR is supported by our generous sponsors:

    Mila   Vector   CIFAR