[2603.22429] Neural Structure Embedding for Symbolic Regression via Continuous Structure Search and Coefficient Optimization

Computer Science > Machine Learning arXiv:2603.22429 (cs) [Submitted on 23 Mar 2026] Title:Neural Structure Embedding for Symbolic Regression via Continuous Structure Search and Coefficient Optimization Authors:Fateme Memar, Tao Zhe, Dongjie Wang View a PDF of the paper titled Neural Structure Embedding for Symbolic Regression via Continuous Structure Search and Coefficient Optimization, by Fateme Memar and 2 other authors View PDF HTML (experimental) Abstract:Symbolic regression aims to discover human-interpretable equations that explain observational data. However, existing approaches rely heavily on discrete structure search (e.g., genetic programming), which often leads to high computational cost, unstable performance, and limited scalability to large equation spaces. To address these challenges, we propose SRCO, a unified embedding-driven framework for symbolic regression that transforms symbolic structures into a continuous, optimizable representation space. The framework consists of three key components: (1) structure embedding: we first generate a large pool of exploratory equations using traditional symbolic regression algorithms and train a Transformer model to compress symbolic structures into a continuous embedding space; (2) continuous structure search: the embedding space enables efficient exploration using gradient-based or sampling-based optimization, significantly reducing the cost of navigating the combinatorial structure space; and (3) coefficient optimi...