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2020 – today
- 2023
[c9]Jiachen Yang, Tarik Dzanic, Brenden K. Petersen, Jun Kudo, Ketan Mittal, Vladimir Z. Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio V. Kolev, Robert W. Anderson, Daniel M. Faissol:
Reinforcement Learning for Adaptive Mesh Refinement. AISTATS 2023: 5997-6014- [c8]Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden K. Petersen, Daniel M. Faissol, Robert W. Anderson:
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement. AAMAS 2023: 14-22 - [i11]Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, Maimuna S. Majumder, Miles D. Cranmer
, Guilherme Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, Brenden K. Petersen, Ruben Glatt, T. Nathan Mundhenk, C. S. Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, A. Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava:
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR abs/2304.01117 (2023) - 2022
- [c7]Mikel Landajuela, Chak Shing Lee, Jiachen Yang, Ruben Glatt, Cláudio P. Santiago, Ignacio Aravena, Terrell Nathan Mundhenk, Garrett Mulcahy, Brenden K. Petersen:
A Unified Framework for Deep Symbolic Regression. NeurIPS 2022 - [i10]Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden K. Petersen, Daniel M. Faissol, Robert W. Anderson:
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement. CoRR abs/2211.00801 (2022) - 2021
- [c6]Brenden K. Petersen, Mikel Landajuela, T. Nathan Mundhenk, Cláudio Prata Santiago, Sookyung Kim, Joanne Taery Kim:
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. ICLR 2021 - [c5]Mikel Landajuela, Brenden K. Petersen, Sookyung Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol:
Discovering symbolic policies with deep reinforcement learning. ICML 2021: 5979-5989 - [c4]T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen:
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. NeurIPS 2021: 24912-24923 - [i9]Jiachen Yang, Tarik Dzanic, Brenden K. Petersen, Jun Kudo, Ketan Mittal, Vladimir Z. Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio V. Kolev, Robert W. Anderson, Daniel M. Faissol:
Reinforcement Learning for Adaptive Mesh Refinement. CoRR abs/2103.01342 (2021) - [i8]Joanne Taery Kim, Mikel Landajuela, Brenden K. Petersen:
Distilling Wikipedia mathematical knowledge into neural network models. CoRR abs/2104.05930 (2021) - [i7]Mikel Landajuela, Brenden K. Petersen, Soo K. Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol:
Improving exploration in policy gradient search: Application to symbolic optimization. CoRR abs/2107.09158 (2021) - [i6]Brenden K. Petersen, Cláudio P. Santiago, Mikel Landajuela:
Incorporating domain knowledge into neural-guided search. CoRR abs/2107.09182 (2021) - [i5]T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen:
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding. CoRR abs/2111.00053 (2021) - 2020
- [c3]Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol:
Single Episode Policy Transfer in Reinforcement Learning. ICLR 2020 - [c2]Joanne Taery Kim, Sookyung Kim, Brenden K. Petersen:
An Interactive Visualization Platform for Deep Symbolic Regression. IJCAI 2020: 5261-5263
2010 – 2019
- 2019
- [j4]Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol:
Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine. J. Comput. Biol. 26(6): 597-604 (2019) - [i4]Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol:
Single Episode Policy Transfer in Reinforcement Learning. CoRR abs/1910.07719 (2019) - [i3]Jacob F. Pettit, Ruben Glatt, Jonathan R. Donadee, Brenden K. Petersen:
Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning. CoRR abs/1912.03408 (2019) - [i2]Brenden K. Petersen:
Deep symbolic regression: Recovering mathematical expressions from data via policy gradients. CoRR abs/1912.04871 (2019) - 2018
- [j3]Michael B. Mayhew, Brenden K. Petersen, Ana Paula Sales, John D. Greene, Vincent X. Liu, Todd S. Wasson:
Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models. J. Biomed. Informatics 78: 33-42 (2018) - [i1]Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol:
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis. CoRR abs/1802.10440 (2018) - 2016
- [j2]Andrew K. Smith, Brenden K. Petersen, Glen E. P. Ropella, Ryan C. Kennedy, Neil Kaplowitz, Murad Ookhtens, C. Anthony Hunt:
Competing Mechanistic Hypotheses of Acetaminophen-Induced Hepatotoxicity Challenged by Virtual Experiments. PLoS Comput. Biol. 12(12) (2016) - [c1]Brenden K. Petersen, C. Anthony Hunt:
Developing a vision for executing scientifically useful virtual biomedical experiments. SpringSim (ADS) 2016: 8 - 2014
- [j1]Brenden K. Petersen, Glen E. P. Ropella, C. Anthony Hunt:
Toward modular biological models: defining analog modules based on referent physiological mechanisms. BMC Syst. Biol. 8: 95 (2014)
Coauthor Index
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