Genetic Programming Bibliography entries for Miles Cranmer

up to index Created by W.Langdon from gp-bibliography.bib Revision:1.8464

GP coauthors/coeditors: Guilherme Seidyo Imai Aldeia, Hengzhe Zhang, Geoffrey F Bomarito, Alcides Fonseca, Bogdan Burlacu, William La Cava, Fabricio Olivetti de Franca, Marco Virgolin, Michael Kommenda, Maimuna Majumder, Guilherme Jorge Nunes Monteiro Espada, Leon Ingelse, Mikel Landajuela, Brenden Kyle Petersen, Ruben Glatt, T Nathan Mundhenk, Chak Shing Lee, Jacob Dean Hochhalter, David L Randall, Pierre-Alexandre Kamienny, Grant Dick, Alessandro Simon, Jaan Kasak, Meera Machado, Casper Wilstrup,

Genetic Programming Articles by Miles Cranmer

• F. O. de Franca and M. Virgolin and M. Kommenda and M. S. Majumder and M. Cranmer and G. Espada and L. Ingelse and A. Fonseca and M. Landajuela and B. Petersen and R. Glatt and N. Mundhenk and C. S. Lee and J. D. Hochhalter and D. L. Randall and P. Kamienny and H. Zhang and G. Dick and A. Simon and B. Burlacu and Jaan Kasak and Meera Machado and Casper Wilstrup and W. G. La Cava. SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. IEEE Transactions on Evolutionary Computation. Early Access. details

Genetic Programming PhD doctoral thesis Miles Cranmer

• Miles Donald Cranmer. Interpretable Machine Learning for the Physical Sciences. PhD thesis, Department of Astrophysical Sciences, Princeton University, USA, 2023. details

Genetic Programming other entries for Miles Cranmer

• Guilherme S. Imai Aldeia and Hengzhe Zhang and Geoffrey Bomarito and Miles Cranmer and Alcides Fonseca and Bogdan Burlacu and William G. La Cava and Fabricio Olivetti de Franca. Call for Action: towards the next generation of symbolic regression benchmark. 2025. details

• F. O. de Franca and M. Virgolin and M. Kommenda and M. S. Majumder and M. Cranmer and G. Espada and L. Ingelse and A. Fonseca and M. Landajuela and B. Petersen and R. Glatt and N. Mundhenk and C. S. Lee and J. D. Hochhalter and D. L. Randall and P. Kamienny and H. Zhang and G. Dick and A. Simon and B. Burlacu and Jaan Kasak and Meera Machado and Casper Wilstrup and W. G. La Cava. Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. 2023. v3. details

• Miles Cranmer. Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl. 2023. details