Drew Bagnell
- Artificial Intelligence top 10%
- Control and Systems Engineering top 10%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications
- Biomedical Engineering
- Co-authors
- Stéphane RossMatthew T. MasonRobert PaoliniReid SimmonsAndrew Y. NgDavid ApfelbaumJeff SchneiderPaul Vernaza
- Topics
- Machine Learning and Algorithms (4 papers)Reinforcement Learning in Robotics (4 papers)Robot Manipulation and Learning (3 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionControl and Systems Engineering
- Journals
- The International Journal of Robotics ResearcharXiv (Cornell University)Figshare
- Partner nations
- United StatesSwitzerland
In The Last Decade
Drew Bagnell
12 papers receiving 317 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 200
- Control and Systems Engineering 112
- Computer Vision and Pattern Recognition 105
- Computer Networks and Communications 39
- Biomedical Engineering 34
Countries citing papers authored by Drew Bagnell
This map shows the geographic impact of Drew Bagnell'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 Drew Bagnell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Drew Bagnell more than expected).
Fields of papers citing papers by Drew Bagnell
This network shows the impact of papers produced by Drew Bagnell. 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 Drew Bagnell. The network helps show where Drew Bagnell may publish in the future.
Co-authorship network of co-authors of Drew Bagnell
This figure shows the co-authorship network connecting the top 25 collaborators of Drew Bagnell. A scholar is included among the top collaborators of Drew Bagnell 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 Drew Bagnell. Drew Bagnell is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Provably Efficient Imitation Learning from Observation Alone. | 5 |
| 2 | 132 | |
| 3 | 34 | |
| 4 | 17 | |
| 5 | 1 | |
| 6 | 25 | |
| 7 | Efficient high dimensional maximum entropy modeling via symmetric partition functions | 11 |
| 8 | SpeedBoost: Anytime Prediction with Uniform Near-Optimality | 38 |
| 9 | Agnostic System Identification for Model-Based Reinforcement Learning | 13 |
| 10 | 13 | |
| 11 | 28 | |
| 12 | On Local Rewards and Scaling Distributed Reinforcement Learning | 14 |
About Drew Bagnell
Drew Bagnell is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 12 papers that have together received 331 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (4 papers), Reinforcement Learning in Robotics (4 papers) and Robot Manipulation and Learning (3 papers). The work is most often cited by research in Artificial Intelligence (200 citations), Computer Vision and Pattern Recognition (105 citations) and Control and Systems Engineering (112 citations). Drew Bagnell has collaborated with scholars based in United States and Switzerland. Frequent co-authors include Stéphane Ross, Matthew T. Mason, Robert Paolini, Reid Simmons, Andrew Y. Ng, David Apfelbaum, Jeff Schneider, Paul Vernaza, Arun Venkatraman and Dov Katz. Their work appears in journals such as The International Journal of Robotics Research, arXiv (Cornell University) and Figshare.
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.