András Antos
Impact in
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- Advanced Bandit Algorithms Research
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics
- Machine Learning and Algorithms
- Neural Networks and Applications
- Bayesian Methods and Mixture Models
Papers in
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- Machine Learning and Algorithms 11
- Reinforcement Learning in Robotics 5
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- Advanced Bandit Algorithms Research 5
- Co-authors
- Csaba Szepesvári (7 shared papers)Ioannis Kontoyiannis (2 shared papers)Rémi Munos (4 shared papers)András György (4 shared papers)László Györfi (4 shared papers)Gergely Neu (2 shared papers)Luc Devroye (1 shared paper)Gábor Lugosi (2 shared papers)
In The Last Decade
András Antos
17 papers receiving 467 citations
Peers
Comparison fields: 5 of 71
- Management Science and Operations Research 145
- Artificial Intelligence 344
- Statistics and Probability 73
- Computational Theory and Mathematics 72
- Computer Networks and Communications 90
Countries citing papers authored by András Antos
This map shows the geographic impact of András Antos'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 András Antos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites András Antos more than expected).
Fields of papers citing papers by András Antos
This network shows the impact of papers produced by András Antos. 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 András Antos. The network helps show where András Antos may publish in the future.
Co-authors
The 13 scholars most cited alongside András Antos, 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 | 2001 | 151 | |
| 2 | 2007 | 99 | |
| 3 | Fitted Q-iteration in continuous action-space MDPs | 2007 | 68 |
| 4 | Online Markov Decision Processes under Bandit Feedback | 2010 | 39 |
| 5 | 1999 | 29 | |
| 6 | 2014 | 21 | |
| 7 | 2005 | 18 | |
| 8 | 2007 | 17 | |
| 9 | 2010 | 14 | |
| 10 | 2005 | 11 | |
| 11 | 1998 | 9 | |
| 12 | 2012 | 8 | |
| 13 | 2000 | 6 | |
| 14 | 2004 | 4 | |
| 15 | 2002 | 4 | |
| 16 | 2012 | 3 | |
| 17 | 1996 | 2 | |
| 18 | 2002 | 1 | |
| 19 | On nonparametric estimates of the expectation | 2002 | 0 |
| 20 | Adaptive strategy for stratified Monte Carlo sampling | 2015 | 0 |
About András Antos
András Antos is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 20 papers that have together received 504 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (11 papers), Reinforcement Learning in Robotics (5 papers), Optimization and Search Problems (5 papers), Advanced Bandit Algorithms Research (5 papers), Advanced Data Compression Techniques (4 papers), Sparse and Compressive Sensing Techniques (4 papers), Statistical Methods and Inference (3 papers) and Image and Signal Denoising Methods (2 papers). The work is most often cited by research in Management Science and Operations Research (145 citations), Artificial Intelligence (344 citations), Statistics and Probability (73 citations), Computational Theory and Mathematics (72 citations) and Computer Networks and Communications (90 citations). András Antos has collaborated with scholars based in Hungary, Canada and France. Frequent co-authors include Csaba Szepesvári, Ioannis Kontoyiannis, Rémi Munos, András György, László Györfi, Gergely Neu, Luc Devroye, Gábor Lugosi, Varun Grover and Gábor Bartók. Their work appears in journals such as IEEE Transactions on Information Theory, Theoretical Computer Science, Machine Learning, IEEE Transactions on Automatic Control and Random Structures and Algorithms.
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.