Bilal Piot
Impact in
- Artificial Intelligence top 1%
- Reinforcement Learning in Robotics
- Evolutionary Algorithms and Applications
- Adversarial Robustness in Machine Learning
- Automotive Engineering top 5%
- Autonomous Vehicle Technology and Safety
Papers in
-
- Reinforcement Learning in Robotics 10
- Evolutionary Algorithms and Applications 4
- Domain Adaptation and Few-Shot Learning 1
- Adversarial Robustness in Machine Learning 1
- Topic Modeling 1
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- Sports Analytics and Performance 2
- Co-authors
- Tom Schaul (4 shared papers)Dan Horgan (3 shared papers)Mohammad Gheshlaghi Azar (6 shared papers)Will Dabney (4 shared papers)Hado van Hasselt (2 shared papers)Joseph Modayil (2 shared papers)Matteo Hessel (2 shared papers)David Silver (2 shared papers)
- Journals
- International Conference on Machine Learning (2 papers)Lancaster EPrints (Lancaster University) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (2 papers)arXiv (Cornell University) (5 papers)
- Partner nations
- United KingdomUnited StatesFrance
In The Last Decade
Bilal Piot
11 papers receiving 2.0k citations
Bilal Piot's Hit Papers
Peers
Comparison fields: 5 of 108
- Artificial Intelligence 1.2k
- Automotive Engineering 253
- Control and Systems Engineering 424
- Computer Vision and Pattern Recognition 352
- Computer Networks and Communications 335
Countries citing papers authored by Bilal Piot
This map shows the geographic impact of Bilal Piot'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 Bilal Piot with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bilal Piot more than expected).
Fields of papers citing papers by Bilal Piot
This network shows the impact of papers produced by Bilal Piot. 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 Bilal Piot. The network helps show where Bilal Piot may publish in the future.
Co-authors
The 25 scholars most cited alongside Bilal Piot, 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 | Rainbow: Combining Improvements in Deep Reinforcement Learning Hit paper breakdown → | 2018 | 1027 |
| 2 | Deep Q-learning From Demonstrations Hit paper breakdown → | 2018 | 493 |
| 3 | 2017 | 146 | |
| 4 | 2017 | 124 | |
| 5 | Noisy Networks For Exploration | 2018 | 116 |
| 6 | Agent57: Outperforming the Atari Human Benchmark | 2020 | 71 |
| 7 | 2017 | 38 | |
| 8 | 2017 | 15 | |
| 9 | 2019 | 6 | |
| 10 | Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning | 2020 | 6 |
| 11 | 2019 | 3 | |
| 12 | 2024 | 0 | |
| 13 | Nouvelles donnees sur lesvoies de migration et les quartiers d'hiver du pouillot iberique phylloscopus ibericus | 2019 | 0 |
About Bilal Piot
Bilal Piot is a scholar working on Artificial Intelligence, Economics and Econometrics, Sociology and Political Science, Information Systems and Ecology, Evolution, Behavior and Systematics, having authored 13 papers that have together received 2.0k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (10 papers), Evolutionary Algorithms and Applications (4 papers), Sports Analytics and Performance (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Adversarial Robustness in Machine Learning (1 paper), Wildlife Ecology and Conservation (1 paper), Topic Modeling (1 paper) and Avian ecology and behavior (1 paper). The work is most often cited by research in Artificial Intelligence (1.2k citations), Automotive Engineering (253 citations), Control and Systems Engineering (424 citations), Computer Vision and Pattern Recognition (352 citations) and Computer Networks and Communications (335 citations). Bilal Piot has collaborated with scholars based in United Kingdom, United States and France. Frequent co-authors include Tom Schaul, Dan Horgan, Mohammad Gheshlaghi Azar, Will Dabney, Hado van Hasselt, Joseph Modayil, Matteo Hessel, David Silver, Georg Ostrovski and Olivier Pietquin. Their work appears in journals such as International Conference on Machine Learning, Lancaster EPrints (Lancaster University), Proceedings of the AAAI Conference on Artificial Intelligence and arXiv (Cornell University).
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.