| Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients BK Petersen, ML Larma, TN Mundhenk, CP Santiago, SK Kim, JT Kim International Conference on Learning Representations 2021, 0 | 330* | |
| Discovering symbolic policies with deep reinforcement learning M Landajuela, BK Petersen, S Kim, CP Santiago, R Glatt, N Mundhenk, ... International Conference on Machine Learning, 5979-5989, 2021 | 106 | 2021 |
| Symbolic regression via neural-guided genetic programming population seeding TN Mundhenk, M Landajuela, R Glatt, CP Santiago, DM Faissol, ... arXiv preprint arXiv:2111.00053, 2021 | 87 | 2021 |
| A unified framework for deep symbolic regression M Landajuela, CS Lee, J Yang, R Glatt, CP Santiago, I Aravena, ... Advances in Neural Information Processing Systems 35, 33985-33998, 2022 | 56 | 2022 |
| Deep reinforcement learning and simulation as a path toward precision medicine BK Petersen, J Yang, WS Grathwohl, C Cockrell, C Santiago, G An, ... Journal of Computational Biology 26 (6), 597-604, 2019 | 56 | 2019 |
| Symbolic regression via deep reinforcement learning enhanced genetic programming seeding T Mundhenk, M Landajuela, R Glatt, CP Santiago, BK Petersen Advances in Neural Information Processing Systems 34, 24912-24923, 2021 | 48 | 2021 |
| Reinforcement learning for adaptive mesh refinement J Yang, T Dzanic, B Petersen, J Kudo, K Mittal, V Tomov, JS Camier, ... International Conference on Artificial Intelligence and Statistics, 5997-6014, 2023 | 40 | 2023 |
| Single episode policy transfer in reinforcement learning J Yang, B Petersen, H Zha, D Faissol arXiv preprint arXiv:1910.07719, 2019 | 38 | 2019 |
| Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models MB Mayhew, BK Petersen, AP Sales, JD Greene, VX Liu, TS Wasson Journal of biomedical informatics 78, 33-42, 2018 | 35 | 2018 |
| Toward modular biological models: defining analog modules based on referent physiological mechanisms BK Petersen, GEP Ropella, CA Hunt BMC systems biology 8, 1-18, 2014 | 33 | 2014 |
| Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis BK Petersen, J Yang, WS Grathwohl, C Cockrell, C Santiago, G An, ... arXiv preprint arXiv:1802.10440, 2018 | 32 | 2018 |
| Competing mechanistic hypotheses of acetaminophen-induced hepatotoxicity challenged by virtual experiments AK Smith, BK Petersen, GEP Ropella, RC Kennedy, N Kaplowitz, ... PloS computational biology 12 (12), e1005253, 2016 | 29 | 2016 |
| Improving exploration in policy gradient search: Application to symbolic optimization M Landajuela, BK Petersen, SK Kim, CP Santiago, R Glatt, TN Mundhenk, ... arXiv preprint arXiv:2107.09158, 2021 | 22 | 2021 |
| Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning JF Pettit, R Glatt, JR Donadee, BK Petersen arXiv preprint arXiv:1912.03408, 2019 | 20 | 2019 |
| Virtual experiments enable exploring and challenging explanatory mechanisms of immune-mediated P450 down-regulation BK Petersen, GEP Ropella, CA Hunt PloS one 11 (5), e0155855, 2016 | 15 | 2016 |
| Distilling wikipedia mathematical knowledge into neural network models JT Kim, M Landajuela, BK Petersen arXiv preprint arXiv:2104.05930, 2021 | 14 | 2021 |
| Interpretable symbolic regression for data science: analysis of the 2022 competition FO de França, M Virgolin, M Kommenda, MS Majumder, M Cranmer, ... arXiv preprint arXiv:2304.01117, 2023 | 11 | 2023 |
| An interactive visualization platform for deep symbolic regression JT Kim, S Kim, BK Petersen Proceedings of the Twenty-Ninth International Conference on International …, 2021 | 11 | 2021 |
| Developing a vision for executing scientifically useful virtual biomedical experiments. BK Petersen, CA Hunt SpringSim (ADS), 8, 2016 | 11 | 2016 |
| Multi-agent reinforcement learning for adaptive mesh refinement J Yang, K Mittal, T Dzanic, S Petrides, B Keith, B Petersen, D Faissol, ... arXiv preprint arXiv:2211.00801, 2022 | 9 | 2022 |
