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Journal of Machine Learning Research

journal

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General referenceInterpretive dossier study · Crown Archives visual atlas
Record originEnglish Wikipedia
Text licenseCC BY-SA 4.0
Source revisionSep 11, 2026
Entity authorityQ1660383 ↗
Source-derived summary

The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning. It was established in 2000 and the first editor-in-chief was Leslie Kaelbling. The current editors-in-chief are Pradeep Ravikumar (Carnegie Mellon University) and Tong Zhang (University of Illinois Urbana-Champaign), who replaced Francis Bach (Inria) and David Blei (Columbia University).

History

The journal was established as an open-access alternative to the journal Machine Learning. In 2001, forty editorial board members of Machine Learning resigned, saying that in the era of the Internet, it was detrimental for researchers to continue publishing their papers in expensive journals with pay-access archives. The open access model employed by the Journal of Machine Learning Research allows authors to publish articles for free and retain copyright, while archives are freely available online.

Print editions of the journal were published by MIT Press until 2004 and by Microtome Publishing thereafter. From its inception, the journal received no revenue from the print edition and paid no subvention to MIT Press or Microtome Publishing.

In response to the prohibitive costs of arranging workshop and conference proceedings publication with traditional academic publishing companies, the journal launched a proceedings publication arm in 2007 and now publishes proceedings for several leading machine learning conferences, including the International Conference on Machine Learning, COLT, AISTATS, and workshops held at the Conference on Neural Information Processing Systems.

References

Further reading

"Top journals in computer science".

Editorial summary

Begin with the source’s own compact description: “Journal of Machine Learning Research” is journal. The dossier treats that line as a proposition to test through Journal, Machine and Learning, not as a finished interpretation.

Editorial reviewA concise reference frame for defining the subject, testing terminology and identifying the institution closest to the evidence. The current lead gives the account dated anchors—2000, 2001, 2004, 2007—that can be checked directly. The linked authority record independently contributes the date 2000-01-01. For this dossier, Journal, Machine and Learning is the immediate research focus.
Editorial analysis

Why this record matters

The phrase “journal” supplies a clear boundary for inquiry. It also exposes the unanswered questions: who defined that boundary, when it became stable and which sources sit outside it.

Evidence profile

Vocabulary and entity names are the principal evidence signals here, because they determine the precision of every later search. The source revision retrieved here is dated Sep 11, 2026. The linked authority identifier is Q1660383. 7 of 8 selected statements include explicit references; 1 carry qualifiers and 0 use preferred rank. The first chronological checks are 2000, 2001, 2004 and 2007.

Critical limits

A concise general-reference account can conceal disagreements about scope, terminology or the weight assigned to individual sources. The lead is largely declarative, so disagreement and counter-evidence require a deliberate search beyond the opening account. Authority statements aid reconciliation but still require their own references, qualifiers and ranks to be checked.

How to read it

Use the entry as an orientation point, then follow its citations and revision history. Names, dates and institutional relationships should be checked against the original record.

Best used for
  • Subject orientation
  • Search vocabulary
  • Locating named sources
Verify next

The closest primary source, responsible institution and strongest cited specialist reference.

Three-step research path

  1. Establish the record: confirm the title “Journal of Machine Learning Research”, its source revision and the description used here.
  2. Expand the search: follow Journal of Machine Learning Research primary sources, Journal of Machine Learning Research archive and Journal research across catalogues and specialist indexes.
  3. Test the account: compare the strongest cited source with the responsible institution’s current record and note any disagreement.

Questions for further research

  1. Which source most directly establishes the central claim about “Journal of Machine Learning Research”?
  2. What terminology or title could unlock a more precise catalogue search?
  3. Which cited source is closest to the event, object or claim?
Subject index

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Source & attribution

This entry incorporates text from “Journal of Machine Learning Research” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.