DASS: A Solver-Adaptable Dynamic Auxiliary Search Strategy for Symbolic Regression
To this end, we propose DASS, a solver-adaptable Dynamic Auxiliary Search Strategy that improves SR by constructing and refining a high-potential auxiliary search subspace.
ProofPaper ↗
Key points
- Symbolic regression (SR) aims to discover interpretable mathematical expressions from data and plays a key role in scientific discovery.
- We argue that, in addition to designing increasingly complex solver-specific SR strategies, an equally promising direction is to equip them with a shareable auxiliary strategy that can provide reliable search guidance.
- DASS represents the auxiliary search space as a functional subspace spanned by a set of high-potential basis function terms.
- Extensive experiments on LLM-SRBench show that DASS significantly improves the accuracy of original solvers with a manageable increase in runtime.
Sources (1)
- [1]DASS: A Solver-Adaptable Dynamic Auxiliary Search Strategy for Symbolic RegressionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 01:59 PM
To this end, we propose DASS, a solver-adaptable Dynamic Auxiliary Search Strategy that improves SR by constructing and refining a high-potential auxiliary search subspace.
Symbolic regression (SR) aims to discover interpretable mathematical expressions from data and plays a key role in scientific discovery.
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