Journal of Machine Learning Research

2.3k papers and 225.4k indexed citations i.

About

The 2.3k papers published in Journal of Machine Learning Research in the last decades have received a total of 225.4k indexed citations. Papers published in Journal of Machine Learning Research usually cover Artificial Intelligence (1.7k papers), Computer Vision and Pattern Recognition (468 papers) and Statistics and Probability (377 papers) specifically the topics of Machine Learning and Algorithms (398 papers), Sparse and Compressive Sensing Techniques (309 papers) and Face and Expression Recognition (299 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, Michael I. Jordan, Andrew Y. Ng, Ruslan Salakhutdinov, Nitish Srivastava, Ilya Sutskever and Alex Krizhevsky.

In The Last Decade

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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2025