Daniil Ryabko
Impact in
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- Advanced Bandit Algorithms Research
- Artificial Intelligence top 10%
- Machine Learning and Algorithms
- Reinforcement Learning in Robotics
- Algorithms and Data Compression
Papers in
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- Machine Learning and Algorithms 13
- Algorithms and Data Compression 8
- Reinforcement Learning in Robotics 5
- Neural Networks and Applications 5
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- Computability, Logic, AI Algorithms 13
- Co-authors
- Boris Ryabko (9 shared papers)Marcus Hütter (5 shared papers)Ronald Ortner (3 shared papers)Peter Auer (2 shared papers)Rémi Munos (2 shared papers)Jürgen Schmidhuber (1 shared paper)Odalric-Ambrym Maillard (1 shared paper)Alessandro Lazaric (1 shared paper)
- Journals
- Theoretical Computer Science (3 papers)Applied Mathematics Letters (2 papers)IEEE Transactions on Information Theory (2 papers)Journal of Machine Learning Research (2 papers)Problems of Information Transmission (1 paper)
- Partner nations
- FranceSwitzerlandRussia
In The Last Decade
Daniil Ryabko
32 papers receiving 184 citations
Peers
Comparison fields: 5 of 43
- Management Science and Operations Research 55
- Artificial Intelligence 128
- Computational Theory and Mathematics 62
- Statistics and Probability 25
- Mathematical Physics 19
Countries citing papers authored by Daniil Ryabko
This map shows the geographic impact of Daniil Ryabko'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 Daniil Ryabko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniil Ryabko more than expected).
Fields of papers citing papers by Daniil Ryabko
This network shows the impact of papers produced by Daniil Ryabko. 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 Daniil Ryabko. The network helps show where Daniil Ryabko may publish in the future.
Co-authors
The 12 scholars most cited alongside Daniil Ryabko, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 27 | |
| 2 | 2009 | 17 | |
| 3 | 2014 | 13 | |
| 4 | 2008 | 11 | |
| 5 | 2008 | 11 | |
| 6 | 2019 | 10 | |
| 7 | 2011 | 9 | |
| 8 | 2010 | 9 | |
| 9 | 2005 | 9 | |
| 10 | 2007 | 9 | |
| 11 | 2010 | 8 | |
| 12 | 2009 | 7 | |
| 13 | 2007 | 6 | |
| 14 | 2007 | 6 | |
| 15 | 2006 | 5 | |
| 16 | 2014 | 5 | |
| 17 | 2011 | 5 | |
| 18 | 2009 | 4 | |
| 19 | 2006 | 4 | |
| 20 | 2009 | 4 |
About Daniil Ryabko
Daniil Ryabko is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability, Management Science and Operations Research and Control and Systems Engineering, having authored 45 papers that have together received 205 indexed citations. Recurring topics across this work include Computability, Logic, AI Algorithms (13 papers), Machine Learning and Algorithms (13 papers), Statistical Methods and Inference (9 papers), Algorithms and Data Compression (8 papers), Advanced Bandit Algorithms Research (7 papers), Advanced Statistical Process Monitoring (6 papers), Reinforcement Learning in Robotics (5 papers) and Neural Networks and Applications (5 papers). The work is most often cited by research in Management Science and Operations Research (55 citations), Artificial Intelligence (128 citations), Computational Theory and Mathematics (62 citations), Statistics and Probability (25 citations) and Mathematical Physics (19 citations). Daniil Ryabko has collaborated with scholars based in France, Switzerland and Russia. Frequent co-authors include Boris Ryabko, Marcus Hütter, Ronald Ortner, Peter Auer, Rémi Munos, Jürgen Schmidhuber, Odalric-Ambrym Maillard, Alessandro Lazaric, Zhanna Reznikova and Pierre‐Marie Preux. Their work appears in journals such as Theoretical Computer Science, Applied Mathematics Letters, IEEE Transactions on Information Theory, Journal of Machine Learning Research and Problems of Information Transmission.
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.