When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization
The paper discusses the challenges in Symbolic Regression (SR) due to the 'Good Structure, Bad Score' phenomenon. It introduces SAGE-Fit, a new framework designed to improve parameter optimization in SR by leveraging structural and semantic priors. The proposed method shows significant enhancements in evaluation fidelity and overall performance across various SR systems.
- ▪Symbolic Regression is crucial for deriving mathematical equations from data.
- ▪Existing methods struggle with non-convex optimization, leading to poor results.
- ▪SAGE-Fit addresses these issues by using tailored modules for better parameter fitting.
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Computer Science > Machine Learning arXiv:2605.23272 (cs) [Submitted on 22 May 2026] Title:When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization Authors:Boxiao Wang, Kai Li, Zhiwei Chen, Yang Huang, Runxiang Wang, Ziwen Zhang, Yifan Zhang, Jian Cheng View a PDF of the paper titled When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization, by Boxiao Wang and 7 other authors View PDF HTML (experimental) Abstract:Symbolic Regression (SR) plays a central role in scientific knowledge discovery by distilling mathematical equations from observational data.
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