Marek Wydmuch
Impact in
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- Reinforcement Learning in Robotics
- Text and Document Classification Technologies
- Artificial Intelligence in Games
- Machine Learning and Data Classification
- Topic Modeling
- Machine Learning and Algorithms
- Sentiment Analysis and Opinion Mining
Papers in
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- Text and Document Classification Technologies 4
- Machine Learning and Data Classification 3
- Machine Learning and Algorithms 2
- Artificial Intelligence in Games 1
- Reinforcement Learning in Robotics 1
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- Water Systems and Optimization 1
- Co-authors
- Wojciech Jaśkowski (1 shared paper)Krzysztof Dembczyński (5 shared papers)Rohit Babbar (2 shared papers)Róbert Busa‐Fekete (1 shared paper)Willem Waegeman (1 shared paper)Eyke Hüllermeier (1 shared paper)
- Journals
- IEEE Transactions on Games (1 paper)arXiv (Cornell University) (3 papers)International Conference on Artificial Intelligence and Statistics (1 paper)Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (1 paper)
- Partner nations
- PolandUnited StatesFinland
In The Last Decade
Marek Wydmuch
6 papers receiving 78 citations
Peers
Comparison fields: 5 of 25
- Artificial Intelligence 71
- Signal Processing 8
- Computer Vision and Pattern Recognition 15
- Management Science and Operations Research 5
- Information Systems 9
Countries citing papers authored by Marek Wydmuch
This map shows the geographic impact of Marek Wydmuch'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 Marek Wydmuch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marek Wydmuch more than expected).
Fields of papers citing papers by Marek Wydmuch
This network shows the impact of papers produced by Marek Wydmuch. 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 Marek Wydmuch. The network helps show where Marek Wydmuch may publish in the future.
Co-authors
The 6 scholars most cited alongside Marek Wydmuch, 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 | 2018 | 44 | |
| 2 | 2022 | 14 | |
| 3 | 2018 | 14 | |
| 4 | 2021 | 5 | |
| 5 | Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification. | 2019 | 3 |
| 6 | Online probabilistic label trees | 2021 | 1 |
About Marek Wydmuch
Marek Wydmuch is a scholar working on Artificial Intelligence, Civil and Structural Engineering, Computer Vision and Pattern Recognition, Management Science and Operations Research and Biomedical Engineering, having authored 6 papers that have together received 81 indexed citations. Recurring topics across this work include Text and Document Classification Technologies (4 papers), Machine Learning and Data Classification (3 papers), Machine Learning and Algorithms (2 papers), Water Systems and Optimization (1 paper), Face and Expression Recognition (1 paper), Artificial Intelligence in Games (1 paper), Reinforcement Learning in Robotics (1 paper) and Artificial Immune Systems Applications (1 paper). The work is most often cited by research in Artificial Intelligence (71 citations), Signal Processing (8 citations), Computer Vision and Pattern Recognition (15 citations), Management Science and Operations Research (5 citations) and Information Systems (9 citations). Marek Wydmuch has collaborated with scholars based in Poland, United States and Finland. Frequent co-authors include Wojciech Jaśkowski, Krzysztof Dembczyński, Rohit Babbar, Róbert Busa‐Fekete, Willem Waegeman and Eyke Hüllermeier. Their work appears in journals such as IEEE Transactions on Games, arXiv (Cornell University), International Conference on Artificial Intelligence and Statistics and Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
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.