Differentially Private Empirical Risk Minimization.
- Journal
- PubMed
In The Last Decade
doi.org/w74065108 →Countries where authors are citing Differentially Private Empirical Risk Minimization.
This map shows the geographic impact of Differentially Private Empirical Risk Minimization.. 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 Differentially Private Empirical Risk Minimization. with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Differentially Private Empirical Risk Minimization. more than expected).
Fields of papers citing Differentially Private Empirical Risk Minimization.
This network shows the impact of Differentially Private Empirical Risk Minimization.. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Differentially Private Empirical Risk Minimization..
About Differentially Private Empirical Risk Minimization.
This paper, published in 2011, received 416 indexed citations . Written by Kamalika Chaudhuri, Claire Monteleoni and Anand D. Sarwate covering the research area of Artificial Intelligence and Statistics and Probability. It is primarily cited by scholars working on Artificial Intelligence (398 citations), Computer Science Applications (75 citations) and Sociology and Political Science (58 citations). Published in PubMed.
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/w74065108.