  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.
