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Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition
3 April 2023
F. O. França
M. Virgolin
M. Kommenda
M. Majumder
M. Cranmer
G. Espada
L. Ingelse
A. Fonseca
Mikel Landajuela
Brenden K. Petersen
Ruben Glatt
N. Mundhenk
C. S. Lee
J. D. Hochhalter
D. L. Randall
Pierre-Alexandre Kamienny
H. Zhang
Grant Dick
A. Simon
Bogdan Burlacu
Jaan Kasak
Meera Machado
Casper Wilstrup
William La Cava
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Papers citing
"Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition"
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Title
Symbolic Regression for Beyond the Standard Model Physics
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Data types as a more ergonomic frontend for Grammar-Guided Genetic Programming
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Symbolic Regression is NP-hard
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S. Pissis
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Taylor Genetic Programming for Symbolic Regression
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Qiang Lu
Qingyun Yang
Jake Luo
Zhiguang Wang
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28 Apr 2022
End-to-end symbolic regression with transformers
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Stéphane dÁscoli
Guillaume Lample
Franccois Charton
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22 Apr 2022
Automated Learning of Interpretable Models with Quantified Uncertainty
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Patrick E. Leser
N. Strauss
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63
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12 Apr 2022
Contemporary Symbolic Regression Methods and their Relative Performance
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Patryk Orzechowski
Bogdan Burlacu
Fabrício Olivetti de Francca
M. Virgolin
Ying Jin
M. Kommenda
J. Moore
187
261
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29 Jul 2021
Neural Symbolic Regression that Scales
Luca Biggio
Tommaso Bendinelli
Alexander Neitz
Aurelien Lucchi
Giambattista Parascandolo
91
180
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11 Jun 2021
Tabular Data: Deep Learning is Not All You Need
Ravid Shwartz-Ziv
Amitai Armon
LMTD
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06 Jun 2021
An Approach to Symbolic Regression Using Feyn
Kevin René Brolos
Meera Machado
C. Cave
Jaan Kasak
Valdemar Stentoft-Hansen
Victor Galindo Batanero
T. Jelen
Casper Wilstrup
28
45
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12 Apr 2021
PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
Joseph D. Romano
Trang T. Le
William La Cava
John T. Gregg
Daniel J. Goldberg
Natasha L. Ray
Praneel Chakraborty
Daniel Himmelstein
Weixuan Fu
J. Moore
GP
43
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30 Nov 2020
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
S. Udrescu
A. Tan
Jiahai Feng
Orisvaldo Neto
Tailin Wu
Max Tegmark
93
191
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18 Jun 2020
Optuna: A Next-generation Hyperparameter Optimization Framework
Takuya Akiba
Shotaro Sano
Toshihiko Yanase
Takeru Ohta
Masanori Koyama
666
5,839
0
25 Jul 2019
Fast, accurate, and transferable many-body interatomic potentials by symbolic regression
Alberto Hernandez
Adarsh Balasubramanian
Fenglin Yuan
Simon Mason
Tim Mueller
53
69
0
01 Apr 2019
Learning Equations for Extrapolation and Control
Subham S. Sahoo
Christoph H. Lampert
Georg Martius
51
234
0
19 Jun 2018
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison
Randal S. Olson
William La Cava
Patryk Orzechowski
Ryan J. Urbanowicz
J. Moore
407
380
0
01 Mar 2017
Differentiable Genetic Programming
Dario Izzo
F. Biscani
Alessio Mereta
37
43
0
15 Nov 2016
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