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Survey paper highlights need for uncertainty quantification in symbolic regression

By PulseAugur Editorial · [1 sources] ·

A new survey paper addresses the critical gap in uncertainty quantification (UQ) for symbolic regression (SR) methods. The paper aims to introduce UQ concepts and review existing literature, categorizing current research into frequentist, Bayesian, and model selection approaches. Despite its importance for model reliability and decision-making, UQ in SR remains an underexplored area, highlighting the need for further research. AI

IMPACT Addresses a key limitation in symbolic regression, potentially enabling more reliable real-world applications.

RANK_REASON The cluster contains a survey paper on a specific research topic within machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey paper highlights need for uncertainty quantification in symbolic regression

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The cluster contains a survey paper on a specific research topic within machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Julia Reuter, Fabricio Olivetti de Franca ·

    Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression

    arXiv:2606.06567v1 Announce Type: new Abstract: Symbolic regression (SR) is a class of methods that systematically explore the space of mathematical functions to discover models that accurately capture the underlying relationships in a dataset. Despite recent advances in the fiel…

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