From Score to Diagnosis: Search-Independent Post-Diagnosis for Symbolic Regression

Key points

• To this end, we propose PostDiag, a search-independent feedback mechanism, which can be shared to different SR solvers.1
• PostDiag introduces Patch-wise Feature Profiling (PFP) to capture local behavior through patch-wise features of level, trend, and roughness.1
• Based on PFP, PostDiag quantifies Skeleton Expressiveness Risk by comparing the fitted candidate with the observations, and Skeleton Stability Risk by measuring variability of behavior after bootstrap refitting.1
• Experiments across multiple LLM-based and GP-based SR solvers show that PostDiag consistently improves accuracy and accelerates convergence.1

Sources (1)

• [1]From Score to Diagnosis: Search-Independent Post-Diagnosis for Symbolic Regression
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 07:54 AM

  Symbolic regression (SR) solvers employ diverse search mechanisms, yet most rely on the same low-information scalar fitness.

  To this end, we propose PostDiag, a search-independent feedback mechanism, which can be shared to different SR solvers.

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