Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression
Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression
你确定吗？符号回归中不确定性量化的全面且易懂的综述
Abstract: Symbolic regression (SR) is a class of methods that systematically explore the space of mathematical functions to discover models that accurately capture the underlying relationships in a dataset. 摘要： 符号回归（SR）是一类通过系统地探索数学函数空间，从而发现能够准确捕捉数据集中潜在关系的模型的算法。
Despite recent advances in the field, a lack of support for uncertainty quantification (UQ) limits its adoption in real-world decision processes. 尽管该领域近期取得了进展，但由于缺乏对不确定性量化（UQ）的支持，限制了其在现实世界决策过程中的应用。
In regression analysis, UQ provides important information about the model reliability, which can both help to avoid overfitting by accounting for uncertainty in the data, and provide insights for decision-making. 在回归分析中，不确定性量化（UQ）提供了关于模型可靠性的重要信息，这不仅可以通过考虑数据中的不确定性来帮助避免过拟合，还能为决策提供洞察。
This survey is the first to clearly address this issue, with the objective of introducing essential UQ concepts and reviewing the current literature on UQ in SR, which can be broadly organized into three research directions: frequentist, Bayesian, and model selection. 本综述首次明确探讨了这一问题，旨在介绍不确定性量化的基本概念，并回顾当前关于符号回归中不确定性量化的文献。这些文献大致可归纳为三个研究方向：频率派方法、贝叶斯方法以及模型选择。
Despite its importance, UQ in SR is still underexplored, which motivates further research into reliable UQ methods for SR. 尽管不确定性量化至关重要，但符号回归中的相关研究仍未得到充分探索，这促使学界进一步研究适用于符号回归的可靠不确定性量化方法。
Paper Details:
- Authors: Julia Reuter, Fabricio Olivetti de Franca
- arXiv ID: 2606.06567
- Subject: Machine Learning (cs.LG)
- Submission Date: 4 Jun 2026
论文详情：
- 作者： Julia Reuter, Fabricio Olivetti de Franca
- arXiv ID： 2606.06567
- 学科： 机器学习 (cs.LG)
- 提交日期： 2026年6月4日