GFN-SR: Symbolic Regression with Generative Flow Networks

• URL: http://arxiv.org/abs/2312.00396v1
• Date: Fri, 1 Dec 2023 07:38:05 GMT
• Title: GFN-SR: Symbolic Regression with Generative Flow Networks
• Authors: Sida Li, Ioana Marinescu, Sebastian Musslick
• Abstract summary: In recent years, deep symbolic regression (DSR) has emerged as a popular method in the field. We propose an alternative framework (GFN-SR) to approach SR with deep learning. GFN-SR is capable of generating a diverse set of best-fitting expressions.
• Score: 0.9208007322096533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Symbolic regression (SR) is an area of interpretable machine learning that aims to identify mathematical expressions, often composed of simple functions, that best fit in a given set of covariates $X$ and response $y$. In recent years, deep symbolic regression (DSR) has emerged as a popular method in the field by leveraging deep reinforcement learning to solve the complicated combinatorial search problem. In this work, we propose an alternative framework (GFN-SR) to approach SR with deep learning. We model the construction of an expression tree as traversing through a directed acyclic graph (DAG) so that GFlowNet can learn a stochastic policy to generate such trees sequentially. Enhanced with an adaptive reward baseline, our method is capable of generating a diverse set of best-fitting expressions. Notably, we observe that GFN-SR outperforms other SR algorithms in noisy data regimes, owing to its ability to learn a distribution of rewards over a space of candidate solutions.

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