Brenden K. Petersen

According to our database, Brenden K. Petersen authored at least 21 papers between 2014 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition.

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

CoRR, 2023

Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement.

Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden K. Petersen, Daniel M. Faissol, Robert W. Anderson

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Reinforcement Learning for Adaptive Mesh Refinement.

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

A Unified Framework for Deep Symbolic Regression.

Mikel Landajuela, Chak Shing Lee, Jiachen Yang, Ruben Glatt, Cláudio P. Santiago, Ignacio Aravena, Terrell Nathan Mundhenk, Garrett Mulcahy, Brenden K. Petersen

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Symbolic Regression via Neural-Guided Genetic Programming Population Seeding.

T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen

CoRR, 2021

Incorporating domain knowledge into neural-guided search.

Brenden K. Petersen, Cláudio P. Santiago, Mikel Landajuela

CoRR, 2021

Improving exploration in policy gradient search: Application to symbolic optimization.

Mikel Landajuela, Brenden K. Petersen, Soo K. Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol

CoRR, 2021

Distilling Wikipedia mathematical knowledge into neural network models.

Joanne Taery Kim, Mikel Landajuela, Brenden K. Petersen

CoRR, 2021

Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding.

T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Discovering symbolic policies with deep reinforcement learning.

Mikel Landajuela, Brenden K. Petersen, Sookyung Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol

Proceedings of the 38th International Conference on Machine Learning, 2021

Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients.

Brenden K. Petersen, Mikel Landajuela, T. Nathan Mundhenk, Cláudio Prata Santiago, Sookyung Kim, Joanne Taery Kim

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

An Interactive Visualization Platform for Deep Symbolic Regression.

Joanne Taery Kim, Sookyung Kim, Brenden K. Petersen

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Single Episode Policy Transfer in Reinforcement Learning.

Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine.

Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol

J. Comput. Biol., 2019

Deep symbolic regression: Recovering mathematical expressions from data via policy gradients.

Brenden K. Petersen

CoRR, 2019

Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning.

Jacob F. Pettit, Ruben Glatt, Jonathan R. Donadee, Brenden K. Petersen

CoRR, 2019

2018

Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models.

Michael B. Mayhew, Brenden K. Petersen, Ana Paula Sales, John D. Greene, Vincent X. Liu, Todd S. Wasson

J. Biomed. Informatics, 2018

Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis.

Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol

CoRR, 2018

2016

Competing Mechanistic Hypotheses of Acetaminophen-Induced Hepatotoxicity Challenged by Virtual Experiments.

Andrew K. Smith, Brenden K. Petersen, Glen E. P. Ropella, Ryan C. Kennedy, Neil Kaplowitz, Murad Ookhtens, C. Anthony Hunt

PLoS Comput. Biol., 2016

Developing a vision for executing scientifically useful virtual biomedical experiments.

Brenden K. Petersen, C. Anthony Hunt

Proceedings of the Agent-Directed Simulation Symposium, 2016

2014

Toward modular biological models: defining analog modules based on referent physiological mechanisms.

Brenden K. Petersen, Glen E. P. Ropella, C. Anthony Hunt

BMC Syst. Biol., 2014