Journal of Machine Learning Research

The MIT Press

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About the journal

Journal of Machine Learning Research is a journal published by The MIT Press in United States. It is listed in PUBMED according to the sources read. Facts are reproduced as recorded at their sources with their capture dates; PublishLens indicators are available after sign-in.

Basic facts

Publisher
The MIT Press
ISSN
1533-7928 · 1532-4435
Country
United States
Access
Not open access / not declared
Official website
http://www.jmlr.org/

Index listings

  • PUBMED · 2002-08-01
Indexing
PUBMED
APC
—
Declared review time (DOAJ)
—
Activity (OpenAlex)
Active — latest works 2026
PublishLens list status
Shown after sign-in
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Methodology signals (dated facts from their sources)

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Journal profile

Descriptive metadata from public sources (DOAJ, OpenAlex, PubMed) as of the capture date shown — contains no assessment or judgment.

Works in OpenAlex

1411

Last recorded publication year

2025

Reported by OpenAlex in 1 of 3 sources (CC0 flags, not verified indexing)DOAJSciELOOpenAlex Core

h-index

132

i10-index

652

From OpenAlex open data (CC0).

Editorial & policy3
Norwegian register (HK-dir)— 2027
Level 2 — top tier (~20% of the field's channels)
Open references deposited at Crossref
0%
ORCID iDs in current deposits
0%
Identity & continuity3
Alternate titles
JMLR
Source type
journal
NLM unique ID (PubMed)
101262635
Classification(no data)

No data available to the platform for this group

No platform data for: Classification — absence is not a judgment about the journal.

Subject areas

  • Neural Networks and Applications
  • Neural Networks and Applications
  • Machine Learning and Algorithms
  • Machine Learning and Algorithms
  • Face and Expression Recognition
  • Face and Expression Recognition
  • Statistical Methods and Inference
  • Statistical Methods and Inference
Show all subjects (50)
  • Sparse and Compressive Sensing Techniques
  • Bayesian Modeling and Causal Inference
  • Sparse and Compressive Sensing Techniques
  • Bayesian Modeling and Causal Inference
  • Machine Learning and Data Classification
  • Machine Learning and Data Classification
  • Bayesian Methods and Mixture Models
  • Bayesian Methods and Mixture Models
  • Gaussian Processes and Bayesian Inference
  • Gaussian Processes and Bayesian Inference
  • Advanced Bandit Algorithms Research
  • Advanced Bandit Algorithms Research
  • Advanced Statistical Methods and Models
  • Advanced Statistical Methods and Models
  • Stochastic Gradient Optimization Techniques
  • Stochastic Gradient Optimization Techniques
  • Fault Detection and Control Systems
  • Fault Detection and Control Systems
  • Reinforcement Learning in Robotics
  • Reinforcement Learning in Robotics
  • Blind Source Separation Techniques
  • Blind Source Separation Techniques
  • Complex Network Analysis Techniques
  • Complex Network Analysis Techniques
  • Control Systems and Identification
  • Control Systems and Identification
  • Data Mining Algorithms and Applications
  • Data Mining Algorithms and Applications
  • Natural Language Processing Techniques
  • Natural Language Processing Techniques
  • Data Management and Algorithms
  • Machine Learning and ELM
  • Machine Learning and ELM
  • Data Management and Algorithms
  • Advanced Clustering Algorithms Research
  • Advanced Clustering Algorithms Research
  • Text and Document Classification Technologies
  • Text and Document Classification Technologies
  • Rough Sets and Fuzzy Logic
  • Rough Sets and Fuzzy Logic
  • Gene expression and cancer classification
  • Gene expression and cancer classification

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The journal's record in other sources:Web of Science MJLDOAJ

Everything shown here is produced automatically from named sources (DOAJ, OpenAlex, Crossref, PubMed, the Norwegian register) as of its capture date, with no human editing. Facts appear as recorded at the source; derived scores (PCI, PTD, PublishLens score) follow a published methodology. Not a judgement on the journal, not an official accreditation, not advice.

Wrong items are corrected automatically by re-reading the source (24 h for daily sources, 7 days for weekly ones) with a dated change log, and each item links to its source record. PublishLens-derived assessments are contested through the appeals mechanism (15 working days). Appeals · Indicator guide

Norwegian register data: Kanalregisteret (HK-dir, Norway) — CC BY 4.0 / NLOD — adapted: level extracted per year · OASPA member list — CC BY

Last automated source read: