Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
This addresses the challenge of discovering mathematical expressions from data for AI researchers, offering a novel approach with competitive performance.
The authors tackled the problem of symbolic regression by proposing a deep learning framework that uses a recurrent neural network to generate mathematical expressions and a risk-seeking policy gradient to optimize them, achieving exact recovery of symbolic expressions on benchmark problems and outperforming baseline methods like Eureqa.
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 optimizes for best-case performance instead of expected performance.