by Sida Li, Ioana Levinscu, Sebastian Musslick
Accepted at NeurIPS 2023 AI4Science Workshop
Source code released soon due to main authors being busy earlier. Stay tuned~
This page currently only contains future roadmap for this project.
- Redesign GFlowNets state space for SR
- Improve LSTM forward policy network
- Find alternative reward function / reward baseline
- Benchmark other training losses for GFlowNets (e.g. detailed balance, flow matching)
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.
@misc{li2023gfnsr,
title={GFN-SR: Symbolic Regression with Generative Flow Networks},
author={Sida Li and Ioana Marinescu and Sebastian Musslick},
year={2023},
eprint={2312.00396},
archivePrefix={arXiv},
primaryClass={cs.LG}
}