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Symbolic Regression is the study of algorithms that automate the search for analytic expressions that fit data.
Jin, Y., Fu, W., Kang, J., Guo, J., & Guo, J. 2019, arXiv preprint arXiv:1910.08892
1910
Earlier work this paper cites.
Buckingham, E. 1914, Physical review, 4, 345
1914
Earlier work this paper cites.
https://proceedings.mlr.press/v202/bauer23a.html
Bauer, J., Baumli, K., Behbahani, F., et al. 2023, in Proceedings of Machine Learning Research, Vol. 202, Proceedings of the 40th International Conference on Machine Learning, ed. A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (PMLR), 1887–1935 · 1935
Earlier work this paper cites.
Feynman, R. P., Leighton, R. B., Sands, M., et al. 1971, The Feynman lectures on physics, Vol. 1-3 (Addison-Wesley Reading, MA)
1971
Earlier work this paper cites.
https://books.google.fr/books?id=XLbvAAAAMAAJ
Weinberg, S. 1972, Gravitation and Cosmology: Principles and Applications of the General Theory of Relativity (Wiley) · 1972
Earlier work this paper cites.
Navarro, J. F., Frenk, C. S., & White, S. D. M. 1996, ApJ, 462, 563, doi: 10.1086/177173
1996
Earlier work this paper cites.
Hochreiter, S., & Schmidhuber, J. 1997, Neural computation, 9, 1735
1997
Earlier work this paper cites.
Zhu, C., Byrd, R. H., Lu, P., & Nocedal, J. 1997, ACM Transactions on mathematical software (TOMS), 23, 550
1997
Earlier work this paper cites.
https://books.google.fr/books?id=tJCuQgAACAAJ
Goldstein, H., Poole, C., & Safko, J. 2002, Classical Mechanics (Addison Wesley) · 2002
Earlier work this paper cites.
Hoai, N., McKay, R., Essam, D., & Chau, R. 2002, in Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600), Vol. 2, 1326–1331 vol.2, doi: 10.1109/CEC.2002.1004435
2002
Earlier work this paper cites.
Wolfram, S. 2003, The mathematica book, Vol. 1 (Wolfram Research, Inc.)
2003
Earlier work this paper cites.
Carilli, C., & Rawlings, S. 2004, New Astronomy Reviews, 48, 979, doi: https://doi.org/10.1016/j.newar.2004.09.001
2004
Earlier work this paper cites.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. 2007, Numerical recipes 3rd edition: The art of scientific computing (Cambridge university press)
2007
Earlier work this paper cites.
Vladislavleva, E. J., Smits, G. F., & den Hertog, D. 2009, IEEE Transactions on Evolutionary Computation, 13, 333, doi: 10.1109/TEVC.2008.926486
2008
Earlier work this paper cites.
Collaboration, L. S. 2009, LSST science book, version 2.0
2009
Earlier work this paper cites.
Manrique, D., Ríos, J., & Rodríguez-Patón, A. 2009, Encyclopedia of Artificial Intelligence, 767
2009
Earlier work this paper cites.
Schmidt, M., & Lipson, H. 2009, science, 324, 81
2009
Earlier work this paper cites.
Binney, J., & Tremaine, S. 2011, Galactic dynamics, Vol. 13 (Princeton university press)
2011
Earlier work this paper cites.
Korns, M. F. 2011, Genetic programming theory and practice VIII, 109
2011
Earlier work this paper cites.
https://arxiv.org/abs/1110.3193
Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, arXiv e-prints, arXiv:1110.3193 · 2011
Earlier work this paper cites.
McConaghy, T. 2011, in Genetic Programming Theory and Practice IX (Springer), 235–260
2011
Earlier work this paper cites.
Panju, M., & Ghodsi, A. 2020, arXiv preprint arXiv:2011.02415
2011
Earlier work this paper cites.
—. 2011, Age-Fitness Pareto Optimization (New York, NY: Springer New York), 129–146, doi: 10.1007/978-1-4419-7747-2_8
2011
Earlier work this paper cites.
https://books.google.fr/books?id=8qHCZjJHRUgC
Jackson, J. 2012, Classical Electrodynamics (Wiley) · 2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
Graham, M. J., Djorgovski, S., Mahabal, A. A., Donalek, C., & Drake, A. J. 2013, Monthly Notices of the Royal Astronomical Society, 431, 2371
2013
Earlier work this paper cites.
Worm, T., & Chiu, K. 2013, in Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, GECCO ’13 (New York, NY, USA: Association for Computing Machinery), 1021–1028, doi: 10.1145/2463372.2463486
2013
Earlier work this paper cites.
Arnaldo, I., Krawiec, K., & O’Reilly, U.-M. 2014, in Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, 879–886
2014
Earlier work this paper cites.
https://books.google.fr/books?id=HbdEAgAAQBAJ
Schwartz, M. 2014, Quantum Field Theory and the Standard Model, Quantum Field Theory and the Standard Model (Cambridge University Press) · 2014
Earlier work this paper cites.
Kingma, D., & Ba, J. 2015, in International Conference on Learning Representations (ICLR), San Diega, CA, USA
2015
Earlier work this paper cites.
https://gplearn.readthedocs.io/en/stable/index.html
Stephens, T. 2015, GPLearn · 2015
Earlier work this paper cites.
Brunton, S. L., Proctor, J. L., & Kutz, J. N. 2016, Proceedings of the National Academy of Sciences, 113, 3932, doi: 10.1073/pnas.1517384113
2016
Earlier work this paper cites.
Gaia Collaboration, Prusti, T., De Bruijne, J., Brown, A. G., et al. 2016, Astronomy & astrophysics, 595, A1
2016
Earlier work this paper cites.
La Cava, W., Danai, K., & Spector, L. 2016, Engineering Applications of Artificial Intelligence, 55, 292
2016
Earlier work this paper cites.
Lu, Z., Pu, H., Wang, F., Hu, Z., & Wang, L. 2017, Advances in neural information processing systems, 30
2017
Earlier work this paper cites.
https://openreview.net/forum?id=BkgRp0FYe
Martius, G., & Lampert, C. H. 2017, Extrapolation and learning equations · 2017
Earlier work this paper cites.
Meurer, A., Smith, C. P., Paprocki, M., et al. 2017, PeerJ Computer Science, 3, e103
2017
Earlier work this paper cites.
https://openreview.net/forum?id=SyWvgP5el
Rajeswaran, A., Ghotra, S., Ravindran, B., & Levine, S. 2017, in International Conference on Learning Representations · 2017
Earlier work this paper cites.
Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M., & Ghiringhelli, L. M. 2018, Phys. Rev. Mater., 2, 083802, doi: 10.1103/PhysRevMaterials.2.083802
2018
Cited alongside, same era.
Sahoo, S., Lampert, C., & Martius, G. 2018, in International Conference on Machine Learning, PMLR, 4442–4450
2018
Cited alongside, same era.
Scolnic, D. M., Jones, D. O., Rest, A., et al. 2018, ApJ, 859, 101, doi: 10.3847/1538-4357/aab9bb
2018
Cited alongside, same era.
Sutton, R. S., & Barto, A. G. 2018, Reinforcement learning: An introduction (MIT press)
2018
Cited alongside, same era.
https://openreview.net/forum?id=Hke-JhA9Y7
Cava, W. L., Singh, T. R., Taggart, J., Suri, S., & Moore, J. 2019, in International Conference on Learning Representations · 2019
Cited alongside, same era.
Ali, M. S., Kshirsagar, M., Naredo, E., & Ryan, C. 2022, in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’22 (New York, NY, USA: Association for Computing Machinery), 902–910, doi: 10.1145/3512290.3528852
2022
Later among the works it cites.
Alnuqaydan, A., Gleyzer, S., & Prosper, H. 2022, Machine Learning: Science and Technology
2022
Later among the works it cites.
https://openreview.net/forum?id=vhrtZYgxLzV
Becker, S., Klein, M., Neitz, A., Parascandolo, G., & Kilbertus, N. 2022, in NeurIPS 2022 AI for Science: Progress and Promises · 2022
Later among the works it cites.
Crochepierre, L., Boudjeloud-Assala, L., & Barbesant, V. 2022, arXiv preprint arXiv:2202.04367
2022
Later among the works it cites.
d’Ascoli, S., Kamienny, P.-A., Lample, G., & Charton, F. 2022, arXiv preprint arXiv:2201.04600
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2019
Cited alongside, same era.
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. 2019, Proceedings of the National Academy of Sciences, 116, 22071
2019
Cited alongside, same era.
Paszke, A., Gross, S., Massa, F., et al. 2019, Advances in neural information processing systems, 32
2019
Cited alongside, same era.
Virgolin, M., Alderliesten, T., & Bosman, P. A. 2019, in Proceedings of the genetic and evolutionary computation conference, 1084–1092
2019
Cited alongside, same era.
Wu, T., & Tegmark, M. 2019, Physical Review E, 100, 033311
2019
Cited alongside, same era.
Željko Ivezić, Kahn, S. M., Tyson, J. A., et al. 2019, The Astrophysical Journal, 873, 111, doi: 10.3847/1538-4357/ab042c
2019
Cited alongside, same era.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., et al. 2020, Information fusion, 58, 82
2020
Cited alongside, same era.
2022
Later among the works it cites.
Delgado, A. M., Wadekar, D., Hadzhiyska, B., et al. 2022, Monthly Notices of the Royal Astronomical Society, 515, 2733
2022
Later among the works it cites.
DiPietro, D. M., & Zhu, B. 2022, arXiv preprint arXiv:2209.01521
2022
Later among the works it cites.
Du, M., Chen, Y., & Zhang, D. 2022, arXiv preprint arXiv:2210.02181
2022
Later among the works it cites.
https://openreview.net/forum?id=rc8o_j8I8PX
Fan, L., Wang, G., Jiang, Y., et al. 2022, in Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track · 2022
Later among the works it cites.
https://openreview.net/forum?id=GoOuIrDHG_Y
Kamienny, P.-A., d’Ascoli, S., Lample, G., & Charton, F. 2022, in Advances in Neural Information Processing Systems, ed. A. H. Oh, A. Agarwal, D. Belgrave, & K. Cho · 2022
Later among the works it cites.
https://openreview.net/forum?id=yeF6cyYU7W
Kamienny, P.-A., & Lamprier, S. 2022, in NeurIPS 2022 AI for Science: Progress and Promises · 2022
Later among the works it cites.
Karagiorgi, G., Kasieczka, G., Kravitz, S., Nachman, B., & Shih, D. 2022, Nature Reviews Physics, 4, 399
2022
Later among the works it cites.
Landajuela, M., Lee, C. S., Yang, J., et al. 2022, Advances in Neural Information Processing Systems, 35, 33985
2022
Later among the works it cites.
Lemos, P., Jeffrey, N., Cranmer, M., Ho, S., & Battaglia, P. 2022, arXiv preprint arXiv:2202.02306
2022
Later among the works it cites.
Luo, C., Chen, C., & Jiang, Z. 2022, International Journal of Computational Methods, 19, 2142002
2022
Later among the works it cites.
Makke, N., & Chawla, S. 2022, arXiv preprint arXiv:2211.10873
2022
Later among the works it cites.
Matchev, K. T., Matcheva, K., & Roman, A. 2022, The Astrophysical Journal, 930, 33
2022
Later among the works it cites.
https://openreview.net/forum?id=oKwyEqClqkb
Matsubara, Y., Chiba, N., Igarashi, R., & Ushiku, Y. 2022, in NeurIPS 2022 AI for Science: Progress and Promises · 2022
Later among the works it cites.
Sabbatini, F., & Calegari, R. 2022, arXiv preprint arXiv:2211.00238
2022
Later among the works it cites.
Shao, H., Villaescusa-Navarro, F., Genel, S., et al. 2022, The Astrophysical Journal, 927, 85
2022
Later among the works it cites.
Usama, M., & Lee, I.-Y. 2022, Sensors, 22, 8240
2022
Later among the works it cites.
2022
Later among the works it cites.
https://openreview.net/forum?id=LTiaPxqe2e
Virgolin, M., & Pissis, S. P. 2022, Transactions on Machine Learning Research · 2022
Later among the works it cites.
Wong, K. W., & Cranmer, M. 2022, arXiv preprint arXiv:2207.12409
2022
Later among the works it cites.
Zheng, W., Sharan, S., Fan, Z., et al. 2022, arXiv preprint arXiv:2212.14849
2022
Later among the works it cites.
Angelis, D., Sofos, F., & Karakasidis, T. E. 2023, Archives of Computational Methods in Engineering, 1
2023
Closest in time.
Bartlett, D. J., Desmond, H., & Ferreira, P. G. 2023, IEEE Transactions on Evolutionary Computation, 1, doi: 10.1109/TEVC.2023.3280250
2023
Closest in time.
Brence, J., Džeroski, S., & Todorovski, L. 2023, Information Sciences, 632, 742, doi: https://doi.org/10.1016/j.ins.2023.03.073
2023
Closest in time.
Burlacu, B. 2023, in Proceedings of the Companion Conference on Genetic and Evolutionary Computation, GECCO ’23 Companion (New York, NY, USA: Association for Computing Machinery), 2412–2419, doi: 10.1145/3583133.3596390
2023
Closest in time.
Desmond, H., Bartlett, D. J., & Ferreira, P. G. 2023, Monthly Notices of the Royal Astronomical Society, 521, 1817, doi: 10.1093/mnras/stad597
2023
Closest in time.
https://proceedings.mlr.press/v202/kamienny23a.html
Kamienny, P., Lample, G., Lamprier, S., & Virgolin, M. 2023, in Proceedings of Machine Learning Research, Vol. 202, International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, ed. A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (PMLR), 15655–15668 · 2023
Closest in time.
Keren, L. S., Liberzon, A., & Lazebnik, T. 2023, Scientific Reports, 13, 1249, doi: 10.1038/s41598-023-28328-2
2023
Closest in time.
https://paperswithcode.com/trends
Papers With Code. 2023, Papers With Code Trends · 2023
Closest in time.
Purcell, T. A., Scheffler, M., & Ghiringhelli, L. M. 2023, arXiv preprint arXiv:2305.01242
2023
Closest in time.
https://openreview.net/forum?id=lheUXtDNvP
Tohme, T., Liu, D., & YOUCEF-TOUMI, K. 2023, Transactions on Machine Learning Research · 2023
Closest in time.
Wadekar, D., Thiele, L., Villaescusa-Navarro, F., et al. 2023, Proceedings of the National Academy of Sciences, 120, e2202074120, doi: 10.1073/pnas.2202074120
2023
Closest in time.
https://proceedings.mlr.press/v202/bendinelli23a.html
Bendinelli, T., Biggio, L., & Kamienny, P. 2023, in Proceedings of Machine Learning Research, Vol. 202, International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, ed. A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (PMLR), 2063–2077 · 2077
Closest in time.