Machine Learning

2.6k papers and 328.5k indexed citations i.

About

The 2.6k papers published in Machine Learning in the last decades have received a total of 328.5k indexed citations. Papers published in Machine Learning usually cover Artificial Intelligence (2.1k papers), Computer Vision and Pattern Recognition (419 papers) and Computational Theory and Mathematics (354 papers) specifically the topics of Machine Learning and Algorithms (577 papers), Machine Learning and Data Classification (414 papers) and Data Mining Algorithms and Applications (217 papers). The most active scholars publishing in Machine Learning are Leo Breiman, Vladimir Vapnik, Corinna Cortes, J. R. Quinlan, Robert E. Schapire, Peter Dayan, Christopher J. Watkins, Richard S. Sutton, Ronald J. Williams and Rich Caruana.

In The Last Decade

Fields of papers published in Machine Learning

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Machine Learning. 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 Machine Learning.

Countries where authors publish in Machine Learning

Since Specialization
Citations

This map shows the geographic impact of research published in Machine Learning. 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 Machine Learning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Machine Learning 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