Citations

This page lists the publications behind the benchmarks and approaches included in the toolkit. Click any entry to expand it and copy the BibTeX.

Benchmarks

Feynman

Udrescu & Tegmark (2020) — AI Feynman: A physics-inspired method for symbolic regression

Udrescu, S.-M. & Tegmark, M. (2020). Science Advances, 6(16), eaay2631.
https://doi.org/10.1126/sciadv.aay2631

@article{Tegmark2020Feynman,
  title = {{AI Feynman: A physics-inspired method for symbolic regression}},
  author = {Udrescu, Silviu-Marian and Tegmark, Max},
  journal = {Science Advances},
  volume = {6},
  number = {16},
  pages = {eaay2631},
  year = {2020},
  publisher = {American Association for the Advancement of Science},
  doi = {10.1126/sciadv.aay2631}
}

SRSD Feynman

Matsubara et al. (2024) — Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery

Matsubara, Y., Chiba, N., Igarashi, R. & Ushiku, Y. (2024). Journal of Data-centric Machine Learning Research.
https://openreview.net/forum?id=qrUdrXsiXX

@article{matsubara2024rethinking,
  title={Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery},
  author={Matsubara, Yoshitomo and Chiba, Naoya and Igarashi, Ryo and Ushiku, Yoshitaka},
  journal={Journal of Data-centric Machine Learning Research},
  year={2024},
  url={https://openreview.net/forum?id=qrUdrXsiXX}
}

Nguyen

Uy et al. (2011) — Semantically-based crossover in genetic programming

Uy, N. Q., Hoai, N. X., O'Neill, M., McKay, R. I. & Galván-López, E. (2011). Genetic Programming and Evolvable Machines, 12(2), 91–119.
https://doi.org/10.1007/s10710-010-9121-2

@article{Uy2011,
  title = {Semantically-based crossover in genetic programming:
             application to real-valued symbolic regression},
  author = {Uy, Nguyen Quang and Hoai, Nguyen Xuan and O'Neill, Michael
             and McKay, R. I. and Galv{\'a}n-L{\'o}pez, Edgar},
  journal = {Genetic Programming and Evolvable Machines},
  volume = {12},
  number = {2},
  pages = {91--119},
  year = {2011},
  month = {Jun},
  doi = {10.1007/s10710-010-9121-2}
}

Approaches

ProGED

Brence et al. (2021) — Probabilistic grammars for equation discovery

Brence, J., Todorovski, L. & Džeroski, S. (2021). Knowledge-Based Systems, 224, 107077.
https://doi.org/10.1016/j.knosys.2021.107077

@article{Brence2021ProGED,
  title   = {Probabilistic grammars for equation discovery},
  author  = {Brence, Jure and Todorovski, Ljup{\v{c}}o and D{\v{z}}eroski, Sa{\v{s}}o},
  journal = {Knowledge-Based Systems},
  volume  = {224},
  pages   = {107077},
  year    = {2021},
  doi     = {10.1016/j.knosys.2021.107077}
}

EDHiE

Mežnar et al. (2023) — Efficient generator of mathematical expressions for symbolic regression

Mežnar, S., Džeroski, S. & Todorovski, L. (2023). Machine Learning.
https://doi.org/10.1007/s10994-023-06400-2

@article{Mežnar2023HVAE,
  title   = {Efficient generator of mathematical expressions for symbolic regression},
  author  = {Me{\v{z}}nar, Sebastian and D{\v{z}}eroski, Sa{\v{s}}o
             and Todorovski, Ljup{\v{c}}o},
  journal = {Machine Learning},
  year    = {2023},
  month   = {Sep},
  issn    = {1573-0565},
  doi     = {10.1007/s10994-023-06400-2}
}