Transactions on Machine Learning Research Review

Transactions on Machine Learning Research (TMLR) is a fully open-access, peer-reviewed scientific journal launched in 2022 as a sister venue to the Journal of Machine Learning Research (JMLR). Operated by Journal of Machine Learning Research Inc., TMLR publishes high-quality empirical and theoretical research in machine learning using a rolling submission workflow hosted on the OpenReview platform.

For computer science and AI researchers evaluating publication options, understanding TMLR’s review philosophy, indexing standing, and governance structure is vital. While traditional computer science publication relies heavily on rigid annual conference deadlines, TMLR provides a continuous, archived venue designed to alleviate review congestion while maintaining rigorous scholarly vetting.

Short Answer: Is TMLR Peer Reviewed?

Yes, Transactions on Machine Learning Research is a legitimate, rigorously peer-reviewed academic journal that operates under an open-review model with double-blind initial evaluation, managed by Action Editors and peer reviewers through OpenReview, and is officially indexed in the Directory of Open Access Journals (DOAJ) and Scopus.

How TMLR’s Peer Review Works

TMLR departs from traditional journal models by emphasizing technical correctness over subjective significance. As articulated in its official editorial policies, the journal evaluates submissions against two fundamental criteria:

Evidence and Technical Soundness: Are the claims made in the submission supported by accurate, convincing, and clear empirical or mathematical evidence?
Community Interest: Would at least some individuals in TMLR’s machine learning audience be interested in knowing the findings of this paper?

Under this philosophy, submissions are not rejected on the grounds of speculative novelty thresholds or lack of broad immediate splash, provided the methodological execution and claims are sound. The peer-review lifecycle proceeds through distinct operational stages:

Rolling Submissions on OpenReview: Submissions are accepted year-round without conference-style deadlines. Authors submit anonymized manuscripts using the official TMLR LaTeX style template.
Action Editor (AE) Assignment: An Action Editor with relevant domain expertise is assigned to manage the review stack, verify initial formatting and conflict-of-interest disclosures, and recruit at least three expert reviewers.
Double-Blind Initial Phase: Reviewers assess manuscripts without knowledge of author identities, and authors do not know the identities of assigned reviewers.
Open Discussion Period: Once reviews are submitted, they become publicly readable on OpenReview alongside author responses and revised manuscript drafts. While reviews and discussions are public, reviewer identities remain anonymous unless a reviewer explicitly chooses to de-anonymize their remarks.
Decision and Revisions: The Action Editor issues one of three formal determinations: Accept, Revise and Resubmit, or Reject. Accepted papers are published immediately in the permanent archival repository.

This transparent framework provides an empirical record of review exchanges, aligning with emerging practices in open peer review and enabling authors to engage in structured dialogue similar to conference rebuttal workflows.

What Is Peer Reviewed and What Is Not

To evaluate the scholarly standing of content published on TMLR, researchers and promotion committees must distinguish between peer-reviewed articles and non-reviewed supplementary materials:

Peer-Reviewed Research Articles: All original research manuscripts, technical contributions, and literature surveys appearing in the archival TMLR volume have completed the full Action Editor and referee evaluation cycle.
Special Certifications: TMLR awards distinct post-acceptance certifications to highlight exceptional contributions. These include Outstanding Certification (jointly awarded by the Editors-in-Chief for contributions equivalent to a conference best paper award), Featured Certification (recognizing top-tier significance), Reproducibility Certification (for rigorous artifact validation), and Survey Certification. These certifications are formal editorial endorsements, not separate publications.
Non-Reviewed Content: Pre-submission discussion comments, public community remarks posted on OpenReview before paper acceptance, and external blog posts discussing TMLR manuscripts are not peer reviewed. Manuscripts that receive a final decision of Reject remain publicly visible on OpenReview as archival submissions but do not constitute published peer-reviewed literature.

Indexing and Recognised Lists

Academic legitimacy and institutional recognition depend heavily on verified coverage in recognized scholarly indexes and databases. As verified on November 7, 2026, TMLR’s indexing status across major lists is as follows:

Indexing Service / Database	Status as of November 2026	Key Details & Identifiers
Directory of Open Access Journals (DOAJ)	Officially Indexed	Online ISSN: 2835-8856; Publisher: Journal of Machine Learning Research Inc.; Diamond OA (0 APC); CC BY licensing.
Scopus	Officially Indexed	Tracked in Scopus database source lists; covered under Computer Science (Artificial Intelligence and Computer Vision).
Web of Science Core Collection	Pending / Emerging Evaluation	Launched in 2022; tracked in secondary citation channels but does not yet possess a formal Clarivate Journal Impact Factor (JIF).
PubMed / PubMed Central	Not Indexed	TMLR does not index in NLM databases, as its scope focuses on computer science and mathematical learning rather than biomedicine.
DBLP Computer Science Bibliography	Fully Indexed	Complete bibliographic records and DOI links for all published volumes are maintained in DBLP.

Because TMLR operates under a diamond open-access model, neither authors nor readers pay access fees or Article Processing Charges (APCs). Research funders and university promotion boards typically view DOAJ and Scopus inclusion alongside sister publication JMLR as clear confirmation of authentic scholarly standards.

How to Check a Specific Article Yourself

Before citing or relying on a paper associated with TMLR, researchers can verify its authentic peer-reviewed status by following these steps:

Locate the Official Archival Record: Navigate to the official TMLR table of contents hosted on https://jmlr.org/tmlr/papers/ or search the publication listing on OpenReview under the TMLR venue portal.
Confirm Final Decision Status: Ensure the paper status displays “Accepted” with an assigned publication date and volume identifier. Manuscripts labeled “Under Review,” “Revisions Requested,” or “Decision: Reject” have not completed peer review.
Verify the Persistent Identifier: Check that the manuscript carries a verified DOI registered through Crossref and an official publication link in the DBLP database.
Inspect OpenReview Reviewer Records: Open the public paper forum on OpenReview to review the Action Editor’s meta-review, official referee ratings, and author revision history to confirm methodological vetting.

Editorial Standards, Retractions, and Governance

TMLR is governed by the same non-profit scholarly foundation that oversees the Journal of Machine Learning Research (JMLR Inc.). The editorial board is led by distinguished researchers including Kyunghyun Cho, Raia Hadsell, and Hugo Larochelle as Editors-in-Chief, supported by a global roster of hundreds of Action Editors across specialized subfields of machine learning and artificial intelligence.

The journal enforces strict publication ethics following guidelines aligned with the Committee on Publication Ethics (COPE):

Dual Submission and Novelty Rules: TMLR requires all submissions to be original. Submissions cannot duplicate published conference papers or archival conference proceedings, preventing dual-publication inflation across the field.
Authorship Quotas: To curb submission spam and reviewer exhaustion, TMLR implements dynamic annual submission quotas per author.
Retraction and Correction Policy: In the event of substantiated data fabrication, severe ethical breaches, or irreproducible theoretical errors, the Editors-in-Chief issue formal public retractions and correction notices linked to the original DOI.

For research teams analyzing academic publication structures across venue categories, consult our guides on computer science conference vs. journal authorship credit and the broader evolution of computational intelligence venues.

Frequently Asked Questions

Is TMLR Scopus indexed?

Yes. Transactions on Machine Learning Research is indexed in Elsevier’s Scopus database under its electronic ISSN 2835-8856, and its citation distributions are tracked in secondary bibliometric rankings including the SCImago Journal Rank (SJR).

Does TMLR have an official Impact Factor?

As of 2026, TMLR does not have an official Clarivate Journal Impact Factor (JIF) in the Journal Citation Reports (JCR). Because the journal launched in 2022, it remains in the citation tracking pipeline for Web of Science Core Collection indexing.

How does TMLR differ from JMLR?

While both are operated by Journal of Machine Learning Research Inc., JMLR serves as an archival venue for expansive, foundational monographs, whereas TMLR focuses on rapid, rolling dissemination of focused research papers. Furthermore, TMLR evaluates papers solely on technical correctness and clear evidence, whereas JMLR emphasizes high subjective impact and comprehensive theoretical depth.

How does the TMLR OpenReview process handle author anonymity?

TMLR utilizes a double-blind initial review phase where author identities are withheld from referees, and reviewer identities are masked from authors. Once initial reviews are submitted, the reviews, rebuttal exchanges, and revisions become publicly visible on OpenReview, while referee identities remain anonymous by default.

Sources

Journal of Machine Learning Research Inc. (2024). Transactions on Machine Learning Research: Editorial Policies. URL: https://jmlr.org/tmlr/editorial-policies.html (Read: 2026-11-07).
Journal of Machine Learning Research Inc. (2024). Transactions on Machine Learning Research: Reviewer Guidelines. URL: https://jmlr.org/tmlr/reviewer-guidelines.html (Read: 2026-11-07).
Directory of Open Access Journals. (2024). Transactions on Machine Learning Research (DOAJ Journal Record). Online ISSN: 2835-8856. URL: https://doaj.org/api/search/journals/issn:2835-8856 (Read: 2026-11-07).
Elsevier. (2024). Scopus Source List: Transactions on Machine Learning Research. URL: https://www.scopus.com/ (Read: 2026-11-07).
Clarivate. (2024). Web of Science Master Journal List. URL: https://mjl.clarivate.com/ (Read: 2026-11-07).