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Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients by Petersen, Brenden K.; Landajuela, Mikel; Mundhenk, T. Nathan; Santiago, Claudio P.; Kim, Soo K.; Kim, Joanne T. is a scholarly article available to read on EtoBox.

What is Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients about?

Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of $\textit{symbolic regression}$. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. We propose a framework that leverages deep learning for symbolic regression via a simple idea: use a large model to search the space of small models. Specifically, we use a recurrent neural network to emit a distribution over tractable mathematical expressions and employ a novel risk-seeking policy gradient to train the network to generate better-fitting expressions. Our algorithm outperforms several baseline methods (including Eureqa, the gold standard for symbolic regression) in its ability to exactly recover symbolic expressions on a series of benchmark problems, both with and without added noise. More broadly, our contributions include a framework that can be applied to optimize hierarchical, variable-length objects under a black-box performance metric, with the ability to incorporate constraints in situ, and a risk-seeking policy gradient formulation that optimiz

Author

Petersen, Brenden K.; Landajuela, Mikel; Mundhenk, T. Nathan; Santiago, Claudio P.; Kim, Soo K.; Kim, Joanne T.

Published

2019

Language

EN