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We propose and study a method for learning interpretable representations for the task of regression.
OpenML: Networked Science in Machine Learning
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Kenneth O. Stanley · 2007
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Neuroevolution: from architectures to learning
Dario Floreano, Peter Dürr, and Claudio Mattiussi · 2008
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Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming
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Ian Goodfellow, Honglak Lee, Quoc V. Le, Andrew Saxe, and Andrew Y. Ng · 2009
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Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, and others · 2011
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Random search for hyper-parameter optimization
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William Whitney · 2016
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Barret Zoph and Quoc V. Le · 2016
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Vernon Austel, Sanjeeb Dash, Oktay Gunluk, Lior Horesh, Leo Liberti, Giacomo Nannicini, and Baruch Schieber · 2017
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Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Effects of constant optimization by nonlinear least squares minimization in symbolic regression
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Ignacio Arnaldo, Krzysztof Krawiec, and Una-May O’Reilly · 2014
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A General Feature Engineering Wrapper for Machine Learning Using \epsilon -Lexicase Survival
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Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and Jason H. Moore · 2017
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Cian Eastwood and Christopher K. I. Williams · 2018
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