Journal of Machine Learning Research

2.2k papers and 205.8k indexed citations

About

The 2.2k papers published in Journal of Machine Learning Research in the last decades have received a total of 205.8k indexed citations. Papers published in Journal of Machine Learning Research usually cover Artificial Intelligence (1.6k papers), Computer Vision and Pattern Recognition (446 papers) and Statistics and Probability (330 papers) specifically the topics of Machine Learning and Algorithms (383 papers), Face and Expression Recognition (287 papers) and Sparse and Compressive Sensing Techniques (271 papers). The most active scholars publishing in Journal of Machine Learning Research are Geoffrey E. Hinton, Laurens van der Maaten, Janez Demšar, David M. Blei, Yoshua Bengio, Michael I. Jordan, Andrew Y. Ng, Nitish Srivastava, Ruslan Salakhutdinov and Ilya Sutskever.

In The Last Decade

Journal of Machine Learning Research

2.0k papers receiving 190.4k citations

Fields of papers published in Journal of Machine Learning Research

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Journal of Machine Learning Research. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Journal of Machine Learning Research.

Countries where authors publish in Journal of Machine Learning Research

Since Specialization
Citations

This map shows the geographic impact of research published in Journal of Machine Learning Research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Journal of Machine Learning Research with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Journal of Machine Learning Research more than expected).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar’s output or impact.

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2026