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 may not yet be accepted in mainstream venues 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:
- 2023.10.18: TMLR partners with the 2023 Machine Learning Reproducibility Challenge.
- 2023.10.07: ICLR 2024 invites authors of TMLR papers with Featured or Outstanding Certifications to present
- 2023.09.12: TMLR papers featured at AutoML 2023 Journal Track
- 2023.08.22: TMLR papers featured at CoLLAs 2023 Journal Track
- 2023.07.10: TMLR Infinite Conference launched
- 2023.07.05: "A Generalist Agent" awarded the first Outstanding Certification
- 2023.01.13: 2022 Annual Report released
- 2022.03.24 TMLR is now accepting submissions
- More news ...
Acting and founding Editors-in-Chief of TMLR are Hugo Larochelle (Google DeepMind, Mila), Raia Hadsell (Google DeepMind), Kyunghyun Cho (Genentech, New York University) and Gautam Kamath (University of Waterloo). TMLR's Managing Editor is Paul Vicol (Google DeepMind), who succedes the founding Managing Editor Fabian Pedregosa (Google DeepMind). 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: email@example.com.
Reviewing and publication
We use the reviewing and publication systems on OpenReview for openness and transparency. See the author guidelines here, or proceed directly to OpenReview to start your submission. TMLR published electronically with International Standard Serial Number (ISSN) 2835-8856.
Advisory Board Members
TMLR’s founding advisory board consists of nine experts who have extensive experience in creating, maintaining and improving academic publication venues, conferences and workshops in machine learning, artificial intelligence, 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:
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