| Deep learning detecting fraud in credit card transactions A Roy, J Sun, R Mahoney, L Alonzi, S Adams, P Beling 2018 systems and information engineering design symposium (SIEDS), 129-134, 2018 | 449 | 2018 |
| A survey of inverse reinforcement learning S Adams, T Cody, PA Beling Artificial Intelligence Review 55 (6), 4307-4346, 2022 | 205 | 2022 |
| Value-decomposition multi-agent actor-critics J Su, S Adams, P Beling Proceedings of the AAAI conference on artificial intelligence 35 (13), 11352 …, 2021 | 174 | 2021 |
| Horse race analysis in credit card fraud—deep learning, logistic regression, and Gradient Boosted Tree G Rushin, C Stancil, M Sun, S Adams, P Beling 2017 systems and information engineering design symposium (SIEDS), 117-121, 2017 | 132 | 2017 |
| A real-time ergonomic monitoring system using the Microsoft Kinect CC Martin, DC Burkert, KR Choi, NB Wieczorek, PM McGregor, ... 2012 IEEE Systems and Information Engineering Design Symposium, 50-55, 2012 | 122 | 2012 |
| An agent based model of the E-Mini S&P 500 applied to Flash Crash analysis M Paddrik, R Hayes, A Todd, S Yang, P Beling, W Scherer 2012 IEEE conference on computational intelligence for financial engineering …, 2012 | 112 | 2012 |
| Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Games X Lin, PA Beling, R Cogill IEEE Transactions Games, 2017 | 111* | 2017 |
| Deep multi-agent reinforcement learning for multi-level preventive maintenance in manufacturing systems J Su, J Huang, S Adams, Q Chang, PA Beling Expert systems with applications 192, 116323, 2022 | 93 | 2022 |
| Adversarial learning in credit card fraud detection MF Zeager, A Sridhar, N Fogal, S Adams, DE Brown, PA Beling 2017 Systems and Information Engineering Design Symposium (SIEDS), 112-116, 2017 | 93 | 2017 |
| Simulating kinect infrared and depth images MJ Landau, BY Choo, PA Beling IEEE Transactions on Cybernetics 46 (12), 3018-3031, 2016 | 87 | 2016 |
| Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning BD Bowes, A Tavakoli, C Wang, A Heydarian, M Behl, PA Beling, ... Journal of Hydroinformatics 23 (3), 529-547, 2021 | 85 | 2021 |
| Dynamic models for floodplain management JR Olsen, PA Beling, JH Lambert Journal of Water Resources Planning and Management 126 (3), 167-175, 2000 | 82 | 2000 |
| An End-to-End Optimal Trade Execution Framework based on Proximal Policy Optimization S Lin, PA Beling Twenty-Ninth International Joint Conference on Artificial Intelligence …, 2020 | 81 | 2020 |
| A survey of feature selection methods for Gaussian mixture models and hidden Markov models S Adams, PA Beling Artificial Intelligence Review 52 (3), 1739-1779, 2019 | 80 | 2019 |
| Feature selection for hidden Markov models and hidden semi-Markov models S Adams, PA Beling, R Cogill IEEE Access 4, 1642-1657, 2016 | 74 | 2016 |
| A dynamic theory of the credit union GM Rubin, GA Overstreet Jr, P Beling, K Rajaratnam Annals of Operations Research 205 (1), 29-53, 2013 | 72 | 2013 |
| Multi-agent inverse reinforcement learning for certain general-sum stochastic games X Lin, SC Adams, PA Beling Journal of Artificial Intelligence Research 66, 473-502, 2019 | 69* | 2019 |
| Modeling law search as prediction F Dadgostari, M Guim, PA Beling, MA Livermore, DN Rockmore Artificial Intelligence and Law 29 (1), 3-34, 2021 | 66* | 2021 |
| AI4SE and SE4AI: A research roadmap T McDermott, D DeLaurentis, P Beling, M Blackburn, M Bone Insight 23 (1), 8-14, 2020 | 66 | 2020 |
| Gaussian process-based algorithmic trading strategy identification SY Yang, Q Qiao, PA Beling, WT Scherer, AA Kirilenko Quantitative Finance 15 (10), 1683-1703, 2015 | 64 | 2015 |
Stephen AdamsResearch Associate Professor, Virginia TechVerified email at vt.edu
