Bilal Piot
- Artificial Intelligence
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Computer Networks and Communications
- Co-authors
- Olivier PietquinMatthieu GeistTom SchaulJoel Z. LeiboGabriel Dulac-ArnoldTodd HesterAudrūnas GruslysIan Osband
- Topics
- Reinforcement Learning in Robotics (3 papers)Emotion and Mood Recognition (1 paper)Speech and dialogue systems (1 paper)
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsarXiv (Cornell University)SPIRE - Sciences Po Institutional REpository
- Partner nations
- FranceUnited KingdomUnited States
In The Last Decade
Bilal Piot
5 papers receiving 126 citations
Peers
Comparison fields: 5 of 41
- Artificial Intelligence 89
- Control and Systems Engineering 47
- Computer Vision and Pattern Recognition 20
- Computational Theory and Mathematics 16
- Computer Networks and Communications 14
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-authorship network of co-authors of Bilal Piot
This figure shows the co-authorship network connecting the top 25 collaborators of Bilal Piot. A scholar is included among the top collaborators of Bilal Piot based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Bilal Piot. Bilal Piot is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 27 | |
| 2 | Learning from Demonstrations for Real World Reinforcement Learning | 43 |
| 3 | 59 | |
| 4 | Imitation Learning Applied to Embodied Conversational Agents | 1 |
| 5 | 2 |
About Bilal Piot
Bilal Piot is a scholar working on Experimental and Cognitive Psychology, Artificial Intelligence and Management Science and Operations Research, having authored 5 papers that have together received 132 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Emotion and Mood Recognition (1 paper) and Speech and dialogue systems (1 paper). The work is most often cited by research in Artificial Intelligence (89 citations), Control and Systems Engineering (47 citations) and Computational Mathematics (1 citation). Bilal Piot has collaborated with scholars based in France, United Kingdom and United States. Frequent co-authors include Olivier Pietquin, Matthieu Geist, Tom Schaul, Joel Z. Leibo, Gabriel Dulac-Arnold, Todd Hester, Audrūnas Gruslys, Ian Osband, Marc Lanctot and John Agapiou. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, arXiv (Cornell University) and SPIRE - Sciences Po Institutional REpository.
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.