Journal of Machine Learning (JMLR) is a US-published open-access peer-reviewed journal in the area of machine learning (ML). Its editor-in-chiefs span across multiple organizations. They are Columbia University in the USA, National Institute for Research in Computer Science and Automation (INRIA) in France, and Max Planck Institute (MPI) for Intelligent Systems in Germany. The fact that its board spans several countries tells of its international nature. The journal was established in 2001.
As defined in its home page, this journal:
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
https://dl.acm.org/journal/jmlr
As such, again, the international nature of this journal is greatly emphasized. According to this source, the journal has about 30% of its papers from international submissions. This shows the board’s commitment to this goal.
The home page definition also emphasizes on the high quality of its content. This is ensured through a rigorous peer-reviewed process. In total, a submitted paper might take more than 6 months to get published. This is in line with the amount of time most top-tier ML conference submissions take.
According to this source, the journal has been gaining popularity. In the last 10 years, the number of citations have doubled from about 2000 citations in 2009 to about 4000 citations in 2019.
According to this source, JMLR was started as a response to the expensive access of research papers in the era of the internet. It was created as an alternative that facilitates free open access, while preserving quality.
I, as a student in North America, had taken for granted the free access of top journals that I enjoy through universities. I do realize now, however, that this is not the case for others in developing countries. With ML research becoming more quintessential than ever, such open access journals are a must to allow this field to grow on a global scale.