--- name: journal-of-machine-learning-research description: 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. --- # 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. ## Scope & topic fit - Machine learning algorithms: supervised, unsupervised, semi-supervised, reinforcement, and self-supervised learning methods with rigorous analysis or strong theoretical grounding. - Statistical learning theory: generalization bounds, PAC learning, information-theoretic analyses, minimax rates. - Optimization for machine learning: convergence analysis, stochastic optimization, second-order methods. - Probabilistic graphical models, Bayesian methods, and approximate inference with theoretical or methodological depth. - Kernel methods, ensemble methods, and classical ML methods — still in scope when the contribution is rigorous. - Software and datasets track (JMLR has separate tracks): significant open-source software or benchmark datasets that serve the ML community. ## Method & evidence bar - Rigor over novelty-signaling: a contribution must be technically correct, well-motivated, and clearly situated relative to the existing literature — the standard is a careful journal referee, not a crowd-sourced conference review. - Theory papers must include correct, complete proofs; informal arguments or proof sketches are insufficient as the primary contribution; clearly label theorems, lemmas, and corollaries. - Empirical papers must demonstrate the method's behavior across multiple settings, include ablations, and characterize when and why the method works; cherry-picked favorable benchmarks are a known failure mode. - Code availability is expected and is integral to JMLR's open-science identity; the software should be usable and documented. - Reproducibility: all hyperparameters, implementation details, and random seeds must be reported; results should be reproducible from the paper and associated code. - JMLR has no page limits; papers should be as long as needed for completeness, and not padded. ## Structure & house style - JMLR publishes in its own open-access format with no mandatory double-column template, but follows standard ML paper conventions; re-check current style files. - The abstract should convey the technical problem, the approach, and the main result concisely; JMLR readers are ML specialists, so technical language is appropriate. - A thorough related-work section is expected and valued: JMLR referees will notice omissions; the section should explain technical relationships, not just list papers. - No length limit: use appendices and supplementary material for proofs, additional experiments, and extended derivations; but the main text should be self-contained for the central claims. - The Software track and Datasets and Benchmarks track have their own submission formats; check current JMLR track instructions. - References should follow JMLR style; re-check the current style guide. ## Official-submission checklist - Before giving submission-ready advice, read `../../resources/source-basis.md` and `../../resources/official-source-map.md`; start from the official source anchors for this journal family, then cite the current journal-specific page you checked. - Visit jmlr.org for the current submission instructions; there is no APC and no standard commercial publisher portal. - Re-check which JMLR track is appropriate (regular, Software, Datasets & Benchmarks) and follow that track's specific requirements. - Confirm code release: a working implementation accessible from the paper is the norm, not the exception. - Ensure all proofs (for theory papers) are complete and correctly stated; plan for extended appendix if needed. - Check the current JMLR policy on prior conference versions of the paper. - Complete JMLR's author disclosure requirements (competing interests, funding). - If the live official instructions conflict with this skill, the official instructions win. ## Pre-submission self-check - [ ] One sentence stating the technical contribution: algorithm, theory result, or method — and what problem it definitively addresses. - [ ] All theoretical claims are supported by complete, formally stated proofs (theorem + proof, not informal argument). - [ ] Empirical evaluation covers multiple settings, baselines, and ablations; implementation details allow reproduction. - [ ] Code is publicly available and documented; the link is included in the paper. - [ ] Related work correctly positions the contribution relative to technically adjacent ML literature. - [ ] If submitting a prior conference version, the extension is clearly documented and meets JMLR policy. ## Common desk-reject triggers - Incremental improvement on a standard benchmark with no theoretical insight, algorithmic novelty, or understanding of why it works. - Theory paper with informal proofs or proof sketches presented as the primary contribution without complete formal arguments. - No code release and no compelling reason for the omission; reproducibility failure. - Related work that ignores technically close prior work in the ML literature. - A paper calibrated to conference hype cycles (incremental SOTA claim, NeurIPS/ICML framing) without the depth and completeness JMLR expects. ## Re-routing decision Papers with computer vision emphasis and archival evaluation → `ieee-transactions-on-pattern-analysis-and-machine-intelligence`. Papers with broader AI significance or real-world application framing for a mixed audience → `nature-machine-intelligence`. Papers with robotics integration and system-level demonstration → `science-robotics`. Fast-paced new results where archival depth is not yet possible → ML conference venues. ## Output format ```text [Fit] High / Medium / Low (one-line reason) [Target] Journal of Machine Learning Research [Topic tags] <2–3 closest topics> [Method/evidence] [Top risk] [Official items to re-check] [Re-route suggestion] ```