Jacob D. Hochhalter

According to our database, Jacob D. Hochhalter authored at least 5 papers between 2022 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

Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations.

Hongsup Oh, Roman Amici, Geoffrey F. Bomarito, Shandian Zhe, Robert M. Kirby, Jacob D. Hochhalter

CoRR, 2023

2022

Automated Learning of Interpretable Models with Quantified Uncertainty.

Geoffrey F. Bomarito, Patrick E. Leser, N. C. M. Strauss, K. M. Garbrecht, Jacob D. Hochhalter

CoRR, 2022

Bingo: a customizable framework for symbolic regression with genetic programming.

David L. Randall, Tyler S. Townsend, Jacob D. Hochhalter, Geoffrey F. Bomarito

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

Bayesian model selection for reducing bloat and overfitting in genetic programming for symbolic regression.

Geoffrey F. Bomarito, Patrick E. Leser, N. C. M. Strauss, K. M. Garbrecht, Jacob D. Hochhalter

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022