# Journal of Machine Learning Research

**Type:** Journals  
**Canonical URL:** https://scholariq.org/journals/journal-of-machine-learning-research/

## Facts

| Field | Value |
| --- | --- |
| Citations | 167,397 |
| h-index | 132 |
| Homepage | http://www.jmlr.org/ |
| Open Access | false |
| ISSN-L | 1532-4435 |
| ISSNs | 1532-4435,1533-7928 |
| OpenAlex ID | https://openalex.org/S118988714 |
| Publisher | The MIT Press |
| Works | 1,412 |

## Journal papers

- [Joint structural estimation of multiple graphical models](https://scholariq.org/papers/joint-structural-estimation-of-multiple-graphical-models/)

## Journal top topics

Showing 8 of 25.

- [Neural Networks and Applications](https://scholariq.org/topics/neural-networks-and-applications/)
- [Machine Learning and Algorithms](https://scholariq.org/topics/machine-learning-and-algorithms/)
- [Face and Expression Recognition](https://scholariq.org/topics/face-and-expression-recognition/)
- [Statistical Methods and Inference](https://scholariq.org/topics/statistical-methods-and-inference/)
- [Sparse and Compressive Sensing Techniques](https://scholariq.org/topics/sparse-and-compressive-sensing-techniques/)
- [Bayesian Modeling and Causal Inference](https://scholariq.org/topics/bayesian-modeling-and-causal-inference/)
- [Machine Learning and Data Classification](https://scholariq.org/topics/machine-learning-and-data-classification/)
- [Bayesian Methods and Mixture Models](https://scholariq.org/topics/bayesian-methods-and-mixture-models/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.