Ruth Urner

About

Ruth Urner is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Ruth Urner has authored 16 papers receiving a total of 119 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Computational Theory and Mathematics. Recurrent topics in Ruth Urner’s work include Machine Learning and Algorithms (10 papers), Machine Learning and Data Classification (6 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Ruth Urner is often cited by papers focused on Machine Learning and Algorithms (10 papers), Machine Learning and Data Classification (6 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Ruth Urner collaborates with scholars based in Canada, Germany and United States. Ruth Urner's co-authors include Shai Ben-David, Gerd Grau, Shai Shalev‐Shwartz, Anastasia Pentina, Ohad Shamir, Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Samory Kpotufe and Haiko Müller and has published in prestigious journals such as Lecture notes in computer science, Discrete Applied Mathematics and Annals of Mathematics and Artificial Intelligence.

In The Last Decade

Co-authorship network of co-authors of Ruth Urner i

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Fields of papers citing papers by Ruth Urner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ruth Urner. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ruth Urner. The network helps show where Ruth Urner may publish in the future.

Countries citing papers authored by Ruth Urner

Since Specialization

Citations

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