Journal of Machine Learning Research
Published by Microtome Publishing
ISSN : 1532-4435 eISSN : 1533-7928
Abbreviation : J. Mach. Learn. Res.
Aims & 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JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or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.
View Aims & ScopeMetrics & Ranking
Impact Factor
| Year | Value |
|---|---|
| 2025 | 5.2 |
Journal Rank
| Year | Value |
|---|---|
| 2024 | 1407 |
Journal Citation Indicator
| Year | Value |
|---|---|
| 2024 | 8482 |
SJR (SCImago Journal Rank)
| Year | Value |
|---|---|
| 2024 | 2.019 |
Quartile
| Year | Value |
|---|---|
| 2024 | Q1 |
h-index
| Year | Value |
|---|---|
| 2024 | 280 |
Impact Factor Trend
Abstracting & Indexing
Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.
Subjects & Keywords
Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Engineering and Mathematics, designed to support cutting-edge academic discovery.
Licensing & Copyright
This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.
Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.