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journal-of-machine-learning-research

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Use when targeting Journal of Machine Learning Research (JMLR) or deciding whether a machine learning methods or theory manuscript fits this open-access venue. Encodes the journal's fit, framing, method-and-evidence bar, house style, official-submission re-check, and desk-reject heuristics.

English-NaturalScience-Journal-Skills/skills/journal-of-machine-learning-research/SKILL.md

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# Journal of Machine Learning Research (journal-of-machine-learning-research)

## Journal positioning

Journal of Machine Learning Research (JMLR) is the primary open-access, society-run archival journal for machine learning research, publishing since 2000 under a non-profit model with no article-processing charges. It is editorially independent, author-friendly in format, and strongly archival in culture: the expectation is rigorous methods and theory, open code, and lasting scientific contribution — not fast-paced competitive benchmarking. JMLR is read by the global ML research community as a reference for foundational methods, algorithms, and theory. It is not a conference proceedings replacement and does not reward framing calibrated to leaderboard position.

This skill is a **fit / venue-selection / re-framing** tool. It does not replace the journal's current official submission guidelines. Before submitting, re-check the live author instructions on the JMLR site (jmlr.org) and the editorial submission system.

## When to trigger

- The author names JMLR as the target venue.
- An ML methods or theory paper requires the archival, no-APC, open-access format and the author is assessing fit versus TPAMI, NeurIPS journal track, or Nature Machine Intelligence.
- A paper provides a rigorous theoretical analysis, a new algorithmic framework, or a well-grounded empirical methodology for which JMLR's archival depth is appropriate.
- The author wants to understand JMLR's editorial culture and rejection profile.
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