We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity. It improves on the previous state-of-the-art by typically being orders of magnitude more robust toward noise and bad data, and also by discovering many formulas that stumped previous methods. We develop a method for discovering generalized symmetries (arbitrary modularity in the computational graph of a formula) from gradient properties of a neural network fit. We use normalizing flows to generalize our symbolic regression method to probability distributions from which we only have samples, and employ statistical hypothesis testing to accelerate robust brute-force search.
@article{arxiv.2006.10782,
title = {AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity},
author = {Silviu-Marian Udrescu and Andrew Tan and Jiahai Feng and Orisvaldo Neto and Tailin Wu and Max Tegmark},
journal= {arXiv preprint arXiv:2006.10782},
year = {2020}
}
Comments
17 pages, 6 figs, replaced to match accepted NeurIPS version