Symbolic Regression with a Learned Concept Library
Arya Grayeli, Atharva Sehgal, Omar Costilla Reyes, Miles Cranmer, Swarat Chaudhuri
Abstract
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 new and powerful scaling law for LLMs.
BibTeX
@inproceedings{
grayeli2024symbolic,
title={Symbolic Regression with a Learned Concept Library},
author={Arya Grayeli and Atharva Sehgal and Omar Costilla Reyes and Miles Cranmer and Swarat Chaudhuri},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=B7S4jJGlvl}
}