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Symbolic regression method introduces partial parameter sharing

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Researchers have developed a new method for symbolic regression that allows for partial parameter sharing across multiple categorical variables. This approach enables the discovery of single expressions that can describe related phenomena while differentiating between universal effects, category-specific trends, and interactions. The method was tested on synthetic data and an astrophysics dataset, demonstrating its ability to achieve similar fit quality with fewer parameters and extract additional information. AI

IMPACT Introduces a novel technique for symbolic regression, potentially enhancing interpretability and efficiency in scientific discovery.

RANK_REASON This is a research paper detailing a new methodology in symbolic regression. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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Symbolic regression method introduces partial parameter sharing

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This is a research paper detailing a new methodology in symbolic regression. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Viktor Martinek, Roland Herzog ·

    Symbolic Regression for Shared Expressions: Introducing Partial Parameter Sharing

    arXiv:2601.04051v3 Announce Type: replace Abstract: Symbolic regression aims to find symbolic expressions that describe datasets. Due to its inherent interpretability, symbolic regression (SR) is a powerful paradigm for scientific discovery. Recent advances have expanded SR to de…

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