Discovering symbolic expressions with parallelized tree search
<|reference_start|>Discovering symbolic expressions with parallelized tree search: Symbolic regression plays a crucial role in modern scientific research thanks to its capability of discovering concise and interpretable mathematical expressions from data. A grand challenge lies in the arduous search for parsimonious and generalizable mathematical formulas, in an infinite search space, while intending to fit the training data. Existing algorithms have faced a critical bottleneck of accuracy and efficiency over a decade when handling problems of complexity, which essentially hinders the pace of applying symbolic regression for scientific exploration across interdisciplinary domains. To this end, we introduce a parallelized tree search (PTS) model to efficiently distill generic mathematical expressions from limited data. Through a series of extensive experiments, we demonstrate the superior accuracy and efficiency of PTS for equation discovery, which greatly outperforms the state-of-the-art baseline models on over 80 synthetic and experimental datasets (e.g., lifting its performance by up to 99% accuracy improvement and one-order of magnitude speed up). PTS represents a key advance in accurate and efficient data-driven discovery of symbolic, interpretable models (e.g., underlying physical laws) and marks a pivotal transition towards scalable symbolic learning.<|reference_end|>
@article{ruan2024discovering,
    title={Discovering symbolic expressions with parallelized tree search},
    author={Kai Ruan, Ze-Feng Gao, Yike Guo, Hao Sun, Ji-Rong Wen, Yang Liu},
    journal={arXiv preprint arXiv:2407.04405},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04405},
    primaryClass={cs.LG cs.AI}
}