Quan Vuong
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Computational Theory and Mathematics
- Industrial and Manufacturing Engineering
- Co-authors
- Dorsa SadighTed XiaoDebidatta DwibediPierre SermanetYevgen ChebotarSergey LevineTianli DingJonathan Tompson
- Topics
- Reinforcement Learning in Robotics (6 papers)Robot Manipulation and Learning (3 papers)Machine Learning and Algorithms (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionControl and Systems EngineeringArtificial Intelligence
- Journals
- arXiv (Cornell University)White Rose Research Online (University of Leeds, The University of Sheffield, University of York)Neural Information Processing Systems
- Partner nations
- United StatesSwitzerlandChina
In The Last Decade
Quan Vuong
15 papers receiving 119 citations
Peers
Comparison fields: 5 of 34
- Artificial Intelligence 52
- Computer Vision and Pattern Recognition 49
- Control and Systems Engineering 47
- Computational Theory and Mathematics 10
- Industrial and Manufacturing Engineering 7
Countries citing papers authored by Quan Vuong
This map shows the geographic impact of Quan Vuong'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 Quan Vuong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Quan Vuong more than expected).
Fields of papers citing papers by Quan Vuong
This network shows the impact of papers produced by Quan Vuong. 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 Quan Vuong. The network helps show where Quan Vuong may publish in the future.
Co-authorship network of co-authors of Quan Vuong
This figure shows the co-authorship network connecting the top 25 collaborators of Quan Vuong. A scholar is included among the top collaborators of Quan Vuong 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 Quan Vuong. Quan Vuong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 29 | |
| 3 | 8 | |
| 4 | 2 | |
| 5 | 5 | |
| 6 | 7 | |
| 7 | 25 | |
| 8 | 5 | |
| 9 | 11 | |
| 10 | 7 | |
| 11 | First Order Constrained Optimization in Policy Space | 2 |
| 12 | 9 | |
| 13 | Efficient entropy for policy gradient with multi-dimensional action space | 4 |
| 14 | 1 | |
| 15 | 3 |
About Quan Vuong
Quan Vuong is a scholar working on Artificial Intelligence, Computer Science Applications and Hardware and Architecture, having authored 15 papers that have together received 124 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Robot Manipulation and Learning (3 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (49 citations), Control and Systems Engineering (47 citations) and Artificial Intelligence (52 citations). Quan Vuong has collaborated with scholars based in United States, Switzerland and China. Frequent co-authors include Dorsa Sadigh, Ted Xiao, Debidatta Dwibedi, Pierre Sermanet, Yevgen Chebotar, Sergey Levine, Tianli Ding, Jonathan Tompson, Suneel Belkhale and Chelsea Finn. Their work appears in journals such as arXiv (Cornell University), White Rose Research Online (University of Leeds, The University of Sheffield, University of York) 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.