新方法让符号回归在高噪声数据中仍能准确找公式。
Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning
- 用动态门控模块过滤噪声，结合强化学习选择符号
- 在高噪声数据上显著优于现有方法，干净数据也表现好
- 适合需要可靠公式发现的科研与工业场景
符号回归（SR）已成为揭示数据内在规律、提升AI模型可解释性的关键技术。然而，当前主流SR方法在高噪声数据上难以正确恢复符号表达式。为此，我们提出一种新型抗噪符号回归（NRSR）方法，通过设计的噪声鲁棒门控模块（NGM）与强化学习（RL）相结合，动态过滤高噪声数据中的无意义信息，实现对符号表达式的有效恢复。该方法引入混合路径熵（MPE）奖励项，增强策略探索能力。实验表明，该方法在高噪声基准上显著优于多个主流基线，在干净数据上亦达领先性能，展现出优异的鲁棒性与有效性。
原文摘要 · Abstract (English)
Symbolic regression (SR) has emerged as a pivotal technique for uncovering the intrinsic information within data and enhancing the interpretability of AI models. However, current state-of-the-art (sota) SR methods struggle to perform correct recovery of symbolic expressions from high-noise data. To address this issue, we introduce a novel noise-resilient SR (NRSR) method capable of recovering expressions from high-noise data. Our method leverages a novel reinforcement learning (RL) approach in conjunction with a designed noise-resilient gating module (NGM) to learn symbolic selection policies. The gating module can dynamically filter the meaningless information from high-noise data, thereby demonstrating a high noise-resilient capability for the SR process. And we also design a mixed path entropy (MPE) bonus term in the RL process to increase the exploration capabilities of the policy. Experimental results demonstrate that our method significantly outperforms several popular baselines on benchmarks with high-noise data. Furthermore, our method also can achieve sota performance on benchmarks with clean data, showcasing its robustness and efficacy in SR tasks.
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