Fetching the paper…
Reading the bibliography…
When choosing between competing symbolic models for a data set, a human will naturally prefer the "simpler" expression or the one which more closely resembles equations previously seen in a similar context.
AI Feynman: A physics-inspired method for symbolic regression
Silviu-Marian Udrescu and Max Tegmark. 2020 · 1905
Earlier work this paper cites.
Minimum Description Length Revisited
Peter Grünwald and Teemu Roos. 2019 · 1908
Earlier work this paper cites.
A New Deterministic Technique for Symbolic Regression
Daniel Rivero and Enrique Fernandez-Blanco. 2019 · 1908
Earlier work this paper cites.
Deep Learning for Symbolic Mathematics
Guillaume Lample and François Charton. 2019 · 1912
Earlier work this paper cites.
An Invariant Form for the Prior Probability in Estimation Problems
Harold Jeffreys. 1946 · 1946
Earlier work this paper cites.
I.—COMPUTING MACHINERY AND INTELLIGENCE
A. M. Turing. 1950 · 1950
Earlier work this paper cites.
THE POPULATION FREQUENCIES OF SPECIES AND THE ESTIMATION OF POPULATION PARAMETERS
I. J. GOOD. 1953 · 1953
Earlier work this paper cites.
Posterior probabilities of alternative linear models. Some theoretical considerations and empirical experiments. [By] F. B. Lempers
F. B. Lempers. 1971 · 1971
Earlier work this paper cites.
Gravitation and Cosmology: Principles and Applications of the General Theory of Relativity
Steven Weinberg. 1972 · 1972
Earlier work this paper cites.
Modeling by shortest data description
J. Rissanen. 1978 · 1978
Earlier work this paper cites.
Estimating the Dimension of a Model
Gideon Schwarz. 1978 · 1978
Earlier work this paper cites.
Reference Posterior Distributions for Bayesian Inference
Jose M. Bernardo. 1979 · 1979
Earlier work this paper cites.
A Universal Prior for Integers and Estimation by Minimum Description Length
Jorma Rissanen. 1983 · 1983
Earlier work this paper cites.
Estimation of probabilities from sparse data for the language model component of a speech recognizer
Slava M. Katz. 1987 · 1987
Earlier work this paper cites.
Estimation and Inference by Compact Coding
C. S. Wallace and P. R. Freeman. 1987 · 1987
Earlier work this paper cites.
Genetic Algorithms in Search, Optimization and Machine Learning
E. David. 1989 · 1989
Earlier work this paper cites.
Elements of Information Theory (2nd ed.)
T. M. Cover and J. A. Thomas. 1991 · 1991
Earlier work this paper cites.
Single-Factor Analysis by Minimum Message Length Estimation
C. S. Wallace and P. R. Freeman. 1992 · 1992
Earlier work this paper cites.
Genetic programming using a minimum description length principle
Hitoshi Iba, Hugo De Garis, and Taisuke Sato. 1994 · 1994
Earlier work this paper cites.
Good-turing frequency estimation without tears
William A. Gale and Geoffrey Sampson. 1995 · 1995
Earlier work this paper cites.
Fractional Bayes Factors for Model Comparison
Anthony O’Hagan. 1995 · 1995
Earlier work this paper cites.
The Intrinsic Bayes Factor for Model Selection and Prediction
James O. Berger and Luis R. Pericchi. 1996 · 1996
Earlier work this paper cites.
Characterization of the Bayes estimator and the MDL estimator for exponential families
J. Takeuchi. 1997 · 1997
Cited alongside, same era.
Classical electrodynamics (3rd ed. ed.)
John David Jackson. 1999 · 1999
Cited alongside, same era.
Foundations of statistical natural language processing
Christopher D. Manning and Hinrich Schutze. 1999 · 1999
Cited alongside, same era.
Schwarz, Wallace, and Rissanen: Intertwining Themes in Theories of Model Selection
Aaron D. Lanterman. 2001 · 2001
Cited alongside, same era.
Classical Mechanics
H. Goldstein, C.P. Poole, and J.L. Safko. 2002 · 2002
Cited alongside, same era.
Practical genetic algorithms (2nd ed.)
R. Haupt and S. Haupt. 2004 · 2004
Cited alongside, same era.
Rademacher Complexity for Enhancing the Generalization of Genetic Programming for Symbolic Regression
Qi Chen, Bing Xue, and Mengjie Zhang. 2022 · 2020
Later among the works it cites.
PySR: Fast & Parallelized Symbolic Regression in Python/Julia
Miles Cranmer. 2020 · 2020
Later among the works it cites.
A Bayesian machine scientist to aid in the solution of challenging scientific problems
Roger Guimerà, Ignasi Reichardt, Antoni Aguilar-Mogas, Francesco A. Massucci, Manuel Miranda, Jordi Pallarès, and Marta Sales-Pardo. 2020 · 2020
Later among the works it cites.
Neural Symbolic Regression that scales. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, 936–945
Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, and Giambattista Parascandolo. 2021 · 2021
Later among the works it cites.
Probabilistic grammars for equation discovery
Jure Brence, Ljupčo Todorovski, and Sašo Džeroski. 2021 · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pareto-Front Exploitation in Symbolic Regression
Guido F. Smits and Mark Kotanchek. 2005 · 2005
Cited alongside, same era.
Discovering Symbolic Models from Deep Learning with Inductive Biases
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, and Shirley Ho. 2020 · 2006
Cited alongside, same era.
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, and Max Tegmark. 2020 · 2006
Cited alongside, same era.
The Minimum Description Length Principle
P. Grunwald. 2007 · 2007
Cited alongside, same era.
Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming
Ekaterina J. Vladislavleva, Guido F. Smits, and Dick den Hertog. 2009 · 2008
Cited alongside, same era.
Accuracy in Symbolic Regression
Michael Korns. 2011 · 2011
Cited alongside, same era.
Later among the works it cites.
Lukas Kammerer, Gabriel Kronberger, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, and Michael Affenzeller. 2021 · 2021
Later among the works it cites.
Complexity Measures for Multi-objective Symbolic Regression
Michael Kommenda, Andreas Beham, Michael Affenzeller, and Gabriel Kronberger. 2021 · 2021
Later among the works it cites.
The Pantheon+ Analysis: The Full Dataset and Light-Curve Release
Dan Scolnic, Dillon Brout, Anthony Carr, Adam G. Riess, Tamara M. Davis, Arianna Dwomoh, David O. Jones, Noor Ali, Pranav Charvu, Rebecca Chen, Erik R. Peterson, Brodie Popovic, Benjamin M. Rose, Charlotte Wood, Peter J. Brown, Ken Chambers, David A. Coulter, Kyle G. Dettman, Georgios Dimitriadis, Alexei V. Filippenko, Ryan J. Foley, Saurabh W. Jha, Charles D. Kilpatrick, Robert P. Kirshner, Yen-Chen Pan, Armin Rest, Cesar Rojas-Bravo, Matthew R. Siebert, Benjamin E. Stahl, and WeiKang Zheng. 2021 · 2021
Later among the works it cites.
SymbolicGPT: A Generative Transformer Model for Symbolic Regression
Mojtaba Valipour, Bowen You, Maysum Panju, and Ali Ghodsi. 2021 · 2021
Later among the works it cites.
Exhaustive Symbolic Regression
Deaglan J. Bartlett, Harry Desmond, and Pedro G. Ferreira. 2022 · 2022
Later among the works it cites.
Discovering ordinary differential equations that govern time-series
Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, and Niki Kilbertus. 2022 · 2022
Later among the works it cites.
Bayesian Model Selection for Reducing Bloat and Overfitting in Genetic Programming for Symbolic Regression. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (Boston, Massachusetts) (GECCO ’22) . Association for Computing Machinery, New York, NY, USA, 526–529
G. F. Bomarito, P. E. Leser, N. C. M. Strauss, K. M. Garbrecht, and J. D. Hochhalter. 2022 · 2022
Later among the works it cites.
Automated learning of interpretable models with quantified uncertainty
G. F. Bomarito, P. E. Leser, N. C. M. Strauss, K. M. Garbrecht, and J. D. Hochhalter. 2023 · 2022
Later among the works it cites.
The Pantheon+ Analysis: Cosmological Constraints
Dillon Brout et al · 2022
Later among the works it cites.
Deep Symbolic Regression for Recurrent Sequences
Stéphane d’Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, and François Charton. 2022 · 2022
Later among the works it cites.
End-to-end symbolic regression with transformers
Pierre-Alexandre Kamienny, Stéphane d’Ascoli, Guillaume Lample, and François Charton. 2022 · 2022
Later among the works it cites.
Rediscovering orbital mechanics with machine learning
Pablo Lemos, Niall Jeffrey, Miles Cranmer, Shirley Ho, and Peter Battaglia. 2022 · 2022
Later among the works it cites.
SymFormer: End-to-end symbolic regression using transformer-based architecture
Martin Vastl, Jonáš Kulhánek, Jiří Kubalík, Erik Derner, and Robert Babuška. 2022 · 2022
Later among the works it cites.
Uncertainty in equation learning. In GECCO ’22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9 - 13, 2022 , Jonathan E. Fieldsend and Markus Wagner (Eds.). ACM, 2298–2305
Matthias Werner, Andrej Junginger, Philipp Hennig, and Georg Martius. 2022 · 2022
Later among the works it cites.
Neural Networks for Symbolic Regression
Jiří Kubalík, Erik Derner, and Robert Babuška. 2023 · 2023
Closest in time.
Transformer-based Planning for Symbolic Regression
Parshin Shojaee, Kazem Meidani, Amir Barati Farimani, and Chandan K. Reddy. 2023 · 2023
Closest in time.
A Comprehensive Measurement of the Local Value of the Hubble Constant with 1 km s -1
Adam G. Riess, Wenlong Yuan, Lucas M. Macri, Dan Scolnic, Dillon Brout, Stefano Casertano, David O. Jones, Yukei Murakami, Gagandeep S. Anand, Louise Breuval, Thomas G. Brink, Alexei V. Filippenko, Samantha Hoffmann, Saurabh W. Jha, W. D’arcy Kenworthy, John Mackenty, Benjamin E. Stahl, and WeiKang Zheng. 2022 · 2041
Closest in time.