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New SMILE framework bridges continuous optimization and discrete symbolic recovery

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SMILE, a novel framework for symbolic regression that combines continuous optimization with discrete symbolic recovery. This hybrid approach first analyzes data to understand the compositional structure of expressions, then uses continuous optimization to learn parameters for an interpretable network, and finally distills this network into a compact, exact symbolic expression. SMILE demonstrates superior robustness and efficiency on the SRBench benchmark, achieving higher symbolic solution rates at significant noise levels and recovering simpler expressions faster than competing methods. AI

IMPACT This research offers a more interpretable and efficient approach to symbolic regression, potentially improving model interpretability and reducing computational costs in data analysis.

RANK_REASON The cluster contains an academic paper detailing a new methodology for symbolic regression. [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 →

New SMILE framework bridges continuous optimization and discrete symbolic recovery

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The cluster contains an academic paper detailing a new methodology for 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) · Mansooreh Montazerin, Antonio Ortega, Ajitesh Srivastava ·

    SMILE: Bridging Continuous Optimization and Discrete Symbolic Recovery

    arXiv:2609.04639v1 Announce Type: new Abstract: Symbolic regression (SR) discovers closed-form mathematical expressions from data, offering interpretability beyond black-box models. Existing methods suffer from slow convergence in combinatorial search spaces and lack mechanisms t…

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