Jacob D. Hochhalter

13 papers Journal 11Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications. CoRR Krishna Prasath Logakannan, Shridhar Vashishtha, Jacob D. Hochhalter, Shandian Zhe, Robert M. Kirby
2025	J	`jnl`	Diffusion-Based Symbolic Regression. CoRR Zachary Bastiani, Robert M. Kirby, Jacob D. Hochhalter, Shandian Zhe
2025	J	`jnl`	Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data. CoRR Keyan Chen, Yile Li, Da Long, Zhitong Xu, Wei Xing, Jacob D. Hochhalter, Shandian Zhe
2025	J	`jnl`	Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data. Trans. Mach. Learn. Res. Keyan Chen, Yile Li, Da Long, Zhitong Xu, Wei W. Xing, Jacob D. Hochhalter, Shandian Zhe
2025	J	`jnl`	SIFBench: An Extensive Benchmark for Fatigue Analysis. CoRR Tushar Gautam, Robert M. Kirby, Jacob D. Hochhalter, Shandian Zhe
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`	Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients. CoRR Zachary Bastiani, Robert M. Kirby, Jacob D. Hochhalter, Shandian Zhe
2024	J	`jnl`	Inherently interpretable machine learning solutions to differential equations. Eng. Comput. Hongsup Oh, Roman Amici, Geoffrey F. Bomarito, Shandian Zhe, Robert M. Kirby, Jacob D. Hochhalter
2023	J	`jnl`	Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations. CoRR Hongsup Oh, Roman Amici, Geoffrey F. Bomarito, Shandian Zhe, Robert M. Kirby, Jacob D. Hochhalter
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
2022	J	`jnl`	Automated Learning of Interpretable Models with Quantified Uncertainty. CoRR Geoffrey F. Bomarito, Patrick E. Leser, N. C. M. Strauss, K. M. Garbrecht, Jacob D. Hochhalter
2022	—	`conf`	Bayesian model selection for reducing bloat and overfitting in genetic programming for symbolic regression. GECCO Companion Geoffrey F. Bomarito, Patrick E. Leser, N. C. M. Strauss, K. M. Garbrecht, Jacob D. Hochhalter
2022	—	`conf`	Bingo: a customizable framework for symbolic regression with genetic programming. GECCO Companion David L. Randall, Tyler S. Townsend, Jacob D. Hochhalter, Geoffrey F. Bomarito