Survey paper highlights need for uncertainty quantification in symbolic regression

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 →

• Symbolic Regression
• Uncertainty Quantification

• paper
• safety

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