Brenden K. Petersen

29 papers A* 7A 2Journal 19Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics. CoRR Conor F. Hayes, Felipe Leno da Silva, Jiachen Yang, T. Nathan Mundhenk, Chak Shing Lee, Jacob F. Pettit, Claudio Santiago, Sookyung Kim, Joanne Taery Kim, Ignacio Aravena Solis, Ruben Glatt, Andre R. Goncalves, Alexander Ladd, Ahmet Can Solak, Thomas Desautels, Daniel M. Faissol, Brenden K. Petersen, Mikel Landajuela
2025	A*	`conf`	DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces. AAAI Jacob F. Pettit, Chak Shing Lee, Jiachen Yang, Alex Ho, Daniel M. Faissol, Brenden K. Petersen, Mikel Landajuela
2025	J	`jnl`	Language model-accelerated deep symbolic optimization. Neural Comput. Appl. Felipe Leno da Silva, Andre R. Goncalves, Sam Nguyen, Denis Vashchenko, Ruben Glatt, Thomas Desautels, Mikel Landajuela, Daniel M. Faissol, Brenden K. Petersen
2025	J	`jnl`	SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. IEEE Trans. Evol. Comput. 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, Chak Shing Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, Hengzhe Zhang, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava
2024	J	`jnl`	DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces. CoRR Jacob F. Pettit, Chak Shing Lee, Jiachen Yang, Alex Ho, Daniel M. Faissol, Brenden K. Petersen, Mikel Landajuela
2023	J	`jnl`	Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR 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, Chak Shing Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava
2023	A	`conf`	Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement. AAMAS Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden K. Petersen, Daniel M. Faissol, Robert W. Anderson
2023	A	`conf`	Reinforcement Learning for Adaptive Mesh Refinement. AISTATS 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
2022	A*	`conf`	A Unified Framework for Deep Symbolic Regression. NeurIPS Mikel Landajuela, Chak Shing Lee, Jiachen Yang, Ruben Glatt, Cláudio P. Santiago, Ignacio Aravena, Terrell Nathan Mundhenk, Garrett Mulcahy, Brenden K. Petersen
2022	J	`jnl`	Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement. CoRR Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden K. Petersen, Daniel M. Faissol, Robert W. Anderson
2021	A*	`conf`	Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. ICLR Brenden K. Petersen, Mikel Landajuela, T. Nathan Mundhenk, Cláudio Prata Santiago, Sookyung Kim, Joanne Taery Kim
2021	A*	`conf`	Discovering symbolic policies with deep reinforcement learning. ICML Mikel Landajuela, Brenden K. Petersen, Sookyung Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol
2021	J	`jnl`	Distilling Wikipedia mathematical knowledge into neural network models. CoRR Joanne Taery Kim, Mikel Landajuela, Brenden K. Petersen
2021	J	`jnl`	Improving exploration in policy gradient search: Application to symbolic optimization. CoRR Mikel Landajuela, Brenden K. Petersen, Soo K. Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol
2021	J	`jnl`	Incorporating domain knowledge into neural-guided search. CoRR Brenden K. Petersen, Cláudio P. Santiago, Mikel Landajuela
2021	J	`jnl`	Reinforcement Learning for Adaptive Mesh Refinement. CoRR 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
2021	A*	`conf`	Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. NeurIPS T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen
2021	J	`jnl`	Symbolic Regression via Neural-Guided Genetic Programming Population Seeding. CoRR T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen
2020	A*	`conf`	An Interactive Visualization Platform for Deep Symbolic Regression. IJCAI Joanne Taery Kim, Sookyung Kim, Brenden K. Petersen
2020	A*	`conf`	Single Episode Policy Transfer in Reinforcement Learning. ICLR Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol
2019	J	`jnl`	Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine. J. Comput. Biol. Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol
2019	J	`jnl`	Deep symbolic regression: Recovering mathematical expressions from data via policy gradients. CoRR Brenden K. Petersen
2019	J	`jnl`	Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning. CoRR Jacob F. Pettit, Ruben Glatt, Jonathan R. Donadee, Brenden K. Petersen
2019	J	`jnl`	Single Episode Policy Transfer in Reinforcement Learning. CoRR Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol
2018	J	`jnl`	Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models. J. Biomed. Informatics Michael B. Mayhew, Brenden K. Petersen, Ana Paula Sales, John D. Greene, Vincent X. Liu, Todd S. Wasson
2018	J	`jnl`	Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis. CoRR Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol
2016	J	`jnl`	Competing Mechanistic Hypotheses of Acetaminophen-Induced Hepatotoxicity Challenged by Virtual Experiments. PLoS Comput. Biol. Andrew K. Smith, Brenden K. Petersen, Glen E. P. Ropella, Ryan C. Kennedy, Neil Kaplowitz, Murad Ookhtens, C. Anthony Hunt
2016	—	`conf`	Developing a vision for executing scientifically useful virtual biomedical experiments. SpringSim (ADS) Brenden K. Petersen, C. Anthony Hunt
2014	J	`jnl`	Toward modular biological models: defining analog modules based on referent physiological mechanisms. BMC Syst. Biol. Brenden K. Petersen, Glen E. P. Ropella, C. Anthony Hunt