Online convex programming and generalized infinitesimal gradient ascent
- Authors
- Martin Zinkevich
- Journal
- International Conference on Machine Learning
In The Last Decade
doi.org/w3616596 →Countries where authors are citing Online convex programming and generalized infinitesimal gradient ascent
This map shows the geographic impact of Online convex programming and generalized infinitesimal gradient ascent. 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 Online convex programming and generalized infinitesimal gradient ascent with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Online convex programming and generalized infinitesimal gradient ascent more than expected).
Fields of papers citing Online convex programming and generalized infinitesimal gradient ascent
This network shows the impact of Online convex programming and generalized infinitesimal gradient ascent. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Online convex programming and generalized infinitesimal gradient ascent.
About Online convex programming and generalized infinitesimal gradient ascent
This paper, published in 2003, received 930 indexed citations . Written by Martin Zinkevich covering the research area of Numerical Analysis, Computational Theory and Mathematics and Management Science and Operations Research. It is primarily cited by scholars working on Artificial Intelligence (600 citations), Management Science and Operations Research (568 citations) and Computer Networks and Communications (298 citations). Published in International Conference on Machine Learning.
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/w3616596.