Roman Ring
Impact in
- Artificial Intelligence top 0.5%
- Reinforcement Learning in Robotics
- Artificial Intelligence in Games
- Adversarial Robustness in Machine Learning
- Topic Modeling
- Explainable Artificial Intelligence (XAI)
- Artificial Intelligence Applications
- Health Informatics top 5%
Papers in
-
- Adversarial Robustness in Machine Learning 1
- Domain Adaptation and Few-Shot Learning 1
- Artificial Intelligence in Games 1
-
- Multimodal Machine Learning Applications 1
- Co-authors
- Francis Song (1 shared paper)John Aslanides (1 shared paper)Amelia Glaese (1 shared paper)Geoffrey Irving (1 shared paper)Ethan Perez (1 shared paper)Trevor Y. Cai (1 shared paper)Saffron Huang (1 shared paper)Nat McAleese (1 shared paper)
- Journals
- Nature (1 paper)Conference on Empirical Methods in Natural Language Processing (EMNLP) (1 paper)neural information processing systems (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Roman Ring
3 papers receiving 2.5k citations
Roman Ring's Hit Papers
Peers
Comparison fields: 5 of 123
- Artificial Intelligence 1.7k
- Health Informatics 38
- Computer Vision and Pattern Recognition 182
- Automotive Engineering 65
- Safety Research 85
Countries citing papers authored by Roman Ring
This map shows the geographic impact of Roman Ring'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 Roman Ring with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roman Ring more than expected).
Fields of papers citing papers by Roman Ring
This network shows the impact of papers produced by Roman Ring. 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 Roman Ring. The network helps show where Roman Ring may publish in the future.
Co-authors
The 8 scholars most cited alongside Roman Ring, 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 | Grandmaster level in StarCraft II using multi-agent reinforcement learning Hit paper breakdown → | 2019 | 2262 |
| 2 | Red Teaming Language Models with Language Models Hit paper breakdown → | 2022 | 189 |
| 3 | 2022 | 167 |
About Roman Ring
Roman Ring is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Nuclear Energy and Engineering, Waste Management and Disposal and Infectious Diseases, having authored 3 papers that have together received 2.6k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (1 paper), Multimodal Machine Learning Applications (1 paper), Domain Adaptation and Few-Shot Learning (1 paper) and Artificial Intelligence in Games (1 paper). The work is most often cited by research in Artificial Intelligence (1.7k citations), Health Informatics (38 citations), Computer Vision and Pattern Recognition (182 citations), Automotive Engineering (65 citations) and Safety Research (85 citations). Roman Ring has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Francis Song, John Aslanides, Amelia Glaese, Geoffrey Irving, Ethan Perez, Trevor Y. Cai, Saffron Huang and Nat McAleese. Their work appears in journals such as Nature, Conference on Empirical Methods in Natural Language Processing (EMNLP) and neural information processing systems.
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.