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Neural Symbolic Regression that Scales

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arxiv 2106.06427 v1 pith:JGGASND7 submitted 2021-06-11 cs.LG

Neural Symbolic Regression that Scales

Luca Biggio , Tommaso Bendinelli , Alexander Neitz , Aurelien Lucchi , Giambattista Parascandolo This is my paper
classification cs.LG
keywords symbolicregressionequationequationstimeapproachcalledcompute
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Symbolic equations are at the core of scientific discovery. The task of discovering the underlying equation from a set of input-output pairs is called symbolic regression. Traditionally, symbolic regression methods use hand-designed strategies that do not improve with experience. In this paper, we introduce the first symbolic regression method that leverages large scale pre-training. We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs. At test time, we query the model on a new set of points and use its output to guide the search for the equation. We show empirically that this approach can re-discover a set of well-known physical equations, and that it improves over time with more data and compute.

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Cited by 3 Pith papers

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  1. LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization

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  2. MOT-SR: Multi-Objective Tool-Augmented Scientific Equation Discovery with Large Language Models

    cs.LG 2026-07 conditional novelty 6.0

    MOT-SR combines tool-augmented data analysis with multi-objective Pareto selection to discover symbolic equations, outperforming LLM-based and classical SR baselines on benchmarks and an EMRI orbital-correction task.

  3. Symbolic Regression for Shared Expressions: Introducing Partial Parameter Sharing

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    Introduces partially-shared parameters for symbolic regression with multiple categorical variables, matching prior fit quality on a supernovae dataset with fewer parameters.