Optimal kernel choice for large-scale two-sample tests
- Journal
- UCL Discovery (University College London)
In The Last Decade
doi.org/w7353508 →Countries where authors are citing Optimal kernel choice for large-scale two-sample tests
This map shows the geographic impact of Optimal kernel choice for large-scale two-sample tests. 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 Optimal kernel choice for large-scale two-sample tests with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Optimal kernel choice for large-scale two-sample tests more than expected).
Fields of papers citing Optimal kernel choice for large-scale two-sample tests
This network shows the impact of Optimal kernel choice for large-scale two-sample tests. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Optimal kernel choice for large-scale two-sample tests.
About Optimal kernel choice for large-scale two-sample tests
This paper, published in 2012, received 312 indexed citations . Written by Arthur Gretton, Dino Sejdinović, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu and Bharath K. Sriperumbudur covering the research area of Statistics and Probability and Computer Networks and Communications. It is primarily cited by scholars working on Artificial Intelligence (172 citations), Computer Vision and Pattern Recognition (84 citations) and Control and Systems Engineering (65 citations). Published in UCL Discovery (University College London).
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.
This paper is also available at doi.org/w7353508.