Dima Kuzmin
Impact in
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- Advanced Bandit Algorithms Research
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- Machine Learning and Algorithms
- Stochastic Gradient Optimization Techniques
- Neural Networks and Applications
Papers in
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- Face and Expression Recognition 1
- Music Technology and Sound Studies 1
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- Blind Source Separation Techniques 1
- Co-authors
- Manfred K. Warmuth (4 shared papers)Chris Donahue (1 shared paper)Aren Jansen (1 shared paper)Kun Su (1 shared paper)Qingqing Huang (1 shared paper)Yu Wang (1 shared paper)Fei Sha (1 shared paper)M. Verzetti (1 shared paper)
- Journals
- Machine Learning (2 papers)Journal of Machine Learning Research (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesSouth KoreaUnited Kingdom
In The Last Decade
Dima Kuzmin
6 papers receiving 96 citations
Peers
Comparison fields: 5 of 33
- Management Science and Operations Research 31
- Artificial Intelligence 73
- Signal Processing 20
- Computational Mechanics 26
- Computer Vision and Pattern Recognition 16
Countries citing papers authored by Dima Kuzmin
This map shows the geographic impact of Dima Kuzmin'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 Dima Kuzmin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dima Kuzmin more than expected).
Fields of papers citing papers by Dima Kuzmin
This network shows the impact of papers produced by Dima Kuzmin. 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 Dima Kuzmin. The network helps show where Dima Kuzmin may publish in the future.
Co-authors
The 11 scholars most cited alongside Dima Kuzmin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Randomized Online PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension | 2008 | 51 |
| 2 | 2009 | 21 | |
| 3 | 2011 | 14 | |
| 4 | 2007 | 12 | |
| 5 | 2024 | 5 | |
| 6 | 2024 | 1 |
About Dima Kuzmin
Dima Kuzmin is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Computational Mechanics, Artificial Intelligence and Management Science and Operations Research, having authored 6 papers that have together received 104 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (2 papers), Sparse and Compressive Sensing Techniques (2 papers), Optimization and Search Problems (1 paper), Face and Expression Recognition (1 paper), Music Technology and Sound Studies (1 paper), Metaheuristic Optimization Algorithms Research (1 paper), Human Motion and Animation (1 paper) and Blind Source Separation Techniques (1 paper). The work is most often cited by research in Management Science and Operations Research (31 citations), Artificial Intelligence (73 citations), Signal Processing (20 citations), Computational Mechanics (26 citations) and Computer Vision and Pattern Recognition (16 citations). Dima Kuzmin has collaborated with scholars based in United States, South Korea and United Kingdom. Frequent co-authors include Manfred K. Warmuth, Chris Donahue, Aren Jansen, Kun Su, Qingqing Huang, Yu Wang, Fei Sha, M. Verzetti, Sumanth Doddapaneni and Joonseok Lee. Their work appears in journals such as Machine Learning, Journal of Machine Learning Research and Proceedings of the AAAI Conference on Artificial Intelligence.
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.