Aaron Wilson

779 total citations
16 papers, 427 citations indexed

About

Aaron Wilson is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Science Applications. According to data from OpenAlex, Aaron Wilson has authored 16 papers receiving a total of 427 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Computer Science Applications. Recurrent topics in Aaron Wilson's work include Reinforcement Learning in Robotics (7 papers), Artificial Intelligence in Games (3 papers) and Advanced Multi-Objective Optimization Algorithms (2 papers). Aaron Wilson is often cited by papers focused on Reinforcement Learning in Robotics (7 papers), Artificial Intelligence in Games (3 papers) and Advanced Multi-Objective Optimization Algorithms (2 papers). Aaron Wilson collaborates with scholars based in United States, United Kingdom and New Zealand. Aaron Wilson's co-authors include Prasad Tadepalli, Alan Fern, Soumya Ray, Margaret Burnett, Laura Beckwith, Gregg Rothermel, Curtis R. Cook, Eric A. Bier, Leilani H. Gilpin and Daniel Davies and has published in prestigious journals such as Journal of Machine Learning Research, ACM SIGOPS Operating Systems Review and AI Magazine.

In The Last Decade

Aaron Wilson

15 papers receiving 391 citations

Peers

Aaron Wilson

AI

SOFTW

CSA

CVPR

CTM

Oleksandr Polozov United States

Benjamin Tan United States

Vytautas Štuikys Lithuania

Robert Duisberg United States

Brad Vander Zanden United States

Assaf Marron Israel

Steve Reeves New Zealand

Harri Hakonen Finland

Börje F. Karlsson China

Bruno C. da Silva Brazil

Oleksandr Polozov United States

Aaron Wilson

321 ×1.3

AI

214 ×2.4

SOFTW

45 ×0.6

CSA

59 ×1.2

CVPR

51 ×1.1

CTM

Citations per year, relative to Aaron Wilson Aaron Wilson (= 1×) peers Oleksandr Polozov

Countries citing papers authored by Aaron Wilson

Since Specialization

Citations

This map shows the geographic impact of Aaron Wilson'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 Aaron Wilson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aaron Wilson more than expected).

Fields of papers citing papers by Aaron Wilson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aaron Wilson. 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 Aaron Wilson. The network helps show where Aaron Wilson may publish in the future.

Co-authorship network of co-authors of Aaron Wilson

This figure shows the co-authorship network connecting the top 25 collaborators of Aaron Wilson. A scholar is included among the top collaborators of Aaron Wilson 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 Aaron Wilson. Aaron Wilson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

1.

Wilson, Aaron, et al.. (2016). Network Explorer. 108–111. 5 indexed citations

2.

Liu, Juan, Eric A. Bier, Aaron Wilson, et al.. (2016). Graph Analysis for Detecting Fraud, Waste, and Abuse in Health‐Care Data. AI Magazine. 37(2). 33–46. 40 indexed citations

3.

Liu, Juan, et al.. (2015). Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data. Proceedings of the AAAI Conference on Artificial Intelligence. 29(2). 3912–3919. 14 indexed citations

4.

Wilson, Aaron, Alan Fern, & Prasad Tadepalli. (2014). Using trajectory data to improve bayesian optimization for reinforcement learning. Journal of Machine Learning Research. 15(1). 253–282. 29 indexed citations

5.

Bos, Nathan, et al.. (2014). Sensemaking in Big Data Environments. 53–55. 2 indexed citations

6.

Liu, Juan, Aaron Wilson, & David Gunning. (2014). Workflow-based Human-in-the-Loop Data Analytics. 49–52. 10 indexed citations

7.

Wilson, Aaron, Alan Fern, & Prasad Tadepalli. (2012). A Bayesian Approach for Policy Learning from Trajectory Preference Queries. Neural Information Processing Systems. 25. 1133–1141. 52 indexed citations

8.

Wilson, Aaron, Alan Fern, & Prasad Tadepalli. (2011). Transfer Learning in Sequential Decision Problems: A Hierarchical Bayesian Approach. International Conference on Machine Learning. 217–227. 16 indexed citations

9.

Wilson, Aaron, Alan Fern, & Prasad Tadepalli. (2010). Bayesian role discovery for multi-agent reinforcement learning. Adaptive Agents and Multi-Agents Systems. 1587–1588. 4 indexed citations

10.

Wilson, Aaron, Alan Fern, & Prasad Tadepalli. (2010). Bayesian Policy Search for Multi-Agent Role Discovery. Proceedings of the AAAI Conference on Artificial Intelligence. 24(1). 624–629. 4 indexed citations

11.

Wilson, Aaron, Alan Fern, Soumya Ray, & Prasad Tadepalli. (2008). Learning and transferring roles in multi-agent MDPs. National Conference on Artificial Intelligence. 55–60. 3 indexed citations

12.

Wilson, Aaron, Alan Fern, Soumya Ray, & Prasad Tadepalli. (2007). Multi-task reinforcement learning. 1015–1022. 130 indexed citations

13.

Wilson, Aaron, et al.. (2003). Harnessing curiosity to increase correctness in end-user programming. 27 indexed citations

14.

Wilson, Aaron, et al.. (2003). Harnessing curiosity to increase correctness in end-user programming. 305–312. 84 indexed citations

15.

Mitchell, Chris J., et al.. (2002). A pragmatic alternative to undetachable signatures. ACM SIGOPS Operating Systems Review. 36(2). 6–11. 6 indexed citations

16.

Mitchell, Chris J., et al.. (2002). On the value of threshold signatures. ACM SIGOPS Operating Systems Review. 36(4). 30–35. 1 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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