JOURNAL OF MACHINE LEARNING RESEARCH
Note: The following journal information is for reference only. Please check the journal website for updated information prior to submission.
Journal Title
JOURNAL OF MACHINE LEARNING RESEARCH
J MACH LEARN RES
ISSN / eISSN
1532-4435 / 1533-7928
Aims and Scope
The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.
JMLR has a commitment to rigorous yet rapid reviewing.
JMLR seeks previously unpublished papers on machine learning that contain:
new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature;
experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems;
accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods;
formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks;
development of new analytical frameworks that advance theoretical studies of practical learning methods;
computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
JMLR has a commitment to rigorous yet rapid reviewing.
JMLR seeks previously unpublished papers on machine learning that contain:
new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature;
experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems;
accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods;
formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks;
development of new analytical frameworks that advance theoretical studies of practical learning methods;
computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
Subject Area
AUTOMATION & CONTROL SYSTEMS
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
CiteScore
9.50
View Trend
CiteScore Ranking
| Category | Quartile | Rank |
|---|---|---|
| Mathematics - Statistics and Probability | Q1 | #7/262 |
| Mathematics - Control and Systems Engineering | Q1 | #31/286 |
| Mathematics - Software | Q1 | #54/404 |
| Mathematics - Artificial Intelligence | Q1 | #52/301 |
Web of Science Core Collection
| Science Citation Index Expanded (SCIE) | Social Sciences Citation Index (SSCI) |
|---|---|
| Indexed | - |
| Category (Journal Citation Reports 2023) | Quartile |
|---|---|
| AUTOMATION & CONTROL SYSTEMS - SCIE | Q1 |
| COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE | Q2 |
H-index
173
Country/Area of Publication
UNITED STATES
Publisher
Microtome Publishing
Publication Frequency
Bimonthly
Year Publication Started
2001
Annual Article Volume
73
Open Access
YES
Contact
MICROTOME PUBL, 31 GIBBS ST, BROOKLINE, USA, MA, 02446
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