Not Every Term Adds New Structure: Sobolev Novelty for Symbolic Regression

arXiv:2609.32597 · cs.LG, cs.AI · Submitted 2026-09-26 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Accuracy — 31× in this paper · explained in 1 episode
• Symbolic Regression — 23× in this paper · explained in 5 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Anisotropy — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Additive Decomposition — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
• Bayesian Symbolic Regression
• Learning concise representations for regression by evolving networks of trees
• Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback
• Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search
• RSRM: Reinforcement Symbolic Regression Machine
• Beyond Accuracy and Complexity: The Effective Information Criterion for Structurally Stable Symbolic Regression

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