Drew Bagnell

2.5k total citations
12 papers, 331 citations indexed

About

Drew Bagnell is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Drew Bagnell has authored 12 papers receiving a total of 331 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 3 papers in Control and Systems Engineering. Recurrent topics in Drew Bagnell's work include Machine Learning and Algorithms (4 papers), Reinforcement Learning in Robotics (4 papers) and Robot Manipulation and Learning (3 papers). Drew Bagnell is often cited by papers focused on Machine Learning and Algorithms (4 papers), Reinforcement Learning in Robotics (4 papers) and Robot Manipulation and Learning (3 papers). Drew Bagnell collaborates with scholars based in United States and Switzerland. Drew Bagnell's 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 and has published in prestigious journals such as The International Journal of Robotics Research, arXiv (Cornell University) and Figshare.

In The Last Decade

Drew Bagnell

12 papers receiving 317 citations

Peers

Drew Bagnell

AI

CSE

CVPR

CNC

BE

Roland Hafner Germany

Roy Fox United States

Tuomas Haarnoja Finland

Zhenhua Huang China

Er Meng Joo Singapore

Beomjoon Kim South Korea

Richard Liaw United States

Ján Vaščák Slovakia

Matteo Turchetta Switzerland

Andrea Caponio Italy

Roland Hafner Germany

Drew Bagnell

266 ×1.3

AI

144 ×1.3

CSE

66 ×0.6

CVPR

27 ×0.7

CNC

54 ×1.6

BE

Citations per year, relative to Drew Bagnell Drew Bagnell (= 1×) peers Roland Hafner

Countries citing papers authored by Drew Bagnell

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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12 of 12 papers shown

1.

Sun, Wen, Anirudh Vemula, Byron Boots, & Drew Bagnell. (2019). Provably Efficient Imitation Learning from Observation Alone.. International Conference on Machine Learning. 6036–6045. 5 indexed citations

2.

Ross, Stéphane & Drew Bagnell. (2018). Efficient Reductions for Imitation Learning. Figshare. 661–668. 132 indexed citations

3.

Mason, Matthew T., et al.. (2018). A convex polynomial model for planar sliding mechanics: theory, application, and experimental validation. The International Journal of Robotics Research. 37(2-3). 249–265. 34 indexed citations

4.

Javdani, Shervin, Yuxin Chen, Amin Karbasi, et al.. (2014). Near Optimal Bayesian Active Learning for Decision Making. arXiv (Cornell University). 33. 430–438. 17 indexed citations

5.

Suppé, Arne, Luis E. Navarro‐Serment, Daniel Urda, Drew Bagnell, & Martial Hebert. (2013). An architecture for online semantic labeling on UGVs. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8741. 87410R–87410R. 1 indexed citations

6.

Katz, Dov, Arun Venkatraman, Moslem Kazemi, Drew Bagnell, & Anthony Stentz. (2013). Perceiving, Learning, and Exploiting Object Affordances for Autonomous Pile Manipulation. 25 indexed citations

7.

Vernaza, Paul & Drew Bagnell. (2012). Efficient high dimensional maximum entropy modeling via symmetric partition functions. Neural Information Processing Systems. 25. 575–583. 11 indexed citations

8.

Bagnell, Drew, et al.. (2012). SpeedBoost: Anytime Prediction with Uniform Near-Optimality. International Conference on Artificial Intelligence and Statistics. 458–466. 38 indexed citations

9.

Ross, Stéphane & Drew Bagnell. (2012). Agnostic System Identification for Model-Based Reinforcement Learning. International Conference on Machine Learning. 1905–1912. 13 indexed citations

10.

Bagnell, Drew, et al.. (2011). Generalized Boosting Algorithms for Convex Optimization. arXiv (Cornell University). 1209–1216. 13 indexed citations

11.

Schneider, Jeff, David Apfelbaum, Drew Bagnell, & Reid Simmons. (2006). Learning Opportunity Costs in Multi-Robot Market Based Planners. Figshare. 1151–1156. 28 indexed citations

12.

Bagnell, Drew & Andrew Y. Ng. (2005). On Local Rewards and Scaling Distributed Reinforcement Learning. Neural Information Processing Systems. 18. 91–98. 14 indexed citations

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.

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