Jacob D. Hochhalter

Orcid: 0000-0003-3607-4573

According to our database1, Jacob D. Hochhalter authored at least 7 papers between 2022 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

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Bibliography

2024
Inherently interpretable machine learning solutions to differential equations.
Eng. Comput., August, 2024

Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients.
CoRR, 2024

2023
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition.
CoRR, 2023

Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations.
CoRR, 2023

2022
Automated Learning of Interpretable Models with Quantified Uncertainty.
CoRR, 2022

Bingo: a customizable framework for symbolic regression with genetic programming.
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.
Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022


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