Symbolic Regression with a Learned Concept Library
<|reference_start|>Symbolic Regression with a Learned Concept Library: We present a novel method for symbolic regression (SR), the task of searching for compact programmatic hypotheses that best explain a dataset. The problem is commonly solved using genetic algorithms; we show that we can enhance such methods by inducing a library of abstract textual concepts. Our algorithm, called LaSR, uses zero-shot queries to a large language model (LLM) to discover and evolve concepts occurring in known high-performing hypotheses. We discover new hypotheses using a mix of standard evolutionary steps and LLM-guided steps (obtained through zero-shot LLM queries) conditioned on discovered concepts. Once discovered, hypotheses are used in a new round of concept abstraction and evolution. We validate LaSR on the Feynman equations, a popular SR benchmark, as well as a set of synthetic tasks. On these benchmarks, LaSR substantially outperforms a variety of state-of-the-art SR approaches based on deep learning and evolutionary algorithms. Moreover, we show that LaSR can be used to discover a novel and powerful scaling law for LLMs.<|reference_end|>
@article{grayeli2024symbolic,
    title={Symbolic Regression with a Learned Concept Library},
    author={Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, Miles Cranmer,
  Swarat Chaudhuri},
    journal={arXiv preprint arXiv:2409.09359},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09359},
    primaryClass={cs.LG cs.AI cs.NE cs.SC}
}