Merging real-valued GOMEA with GP-GOMEA enables simultaneous optimization of constants and expression structure, generally outperforming other constant-handling techniques in symbolic regression.
An Dinh, Stacey Miertschin, Amber Young, and Somya D
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A new conformal framework learns polyhedral uncertainty sets tailored to robust optimization objectives, minimizing decision loss while preserving coverage via calibration and independent re-calibration.
Systematic benchmarking reveals that regression calibration metrics frequently disagree on recalibration quality, with ENCE and CWC identified as more consistent performers.
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Simultaneous Model-Based Evolution of Constants and Expression Structure in GP-GOMEA for Symbolic Regression
Merging real-valued GOMEA with GP-GOMEA enables simultaneous optimization of constants and expression structure, generally outperforming other constant-handling techniques in symbolic regression.
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Learning Polyhedral Conformal Sets for Robust Optimization
A new conformal framework learns polyhedral uncertainty sets tailored to robust optimization objectives, minimizing decision loss while preserving coverage via calibration and independent re-calibration.
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Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Models
Systematic benchmarking reveals that regression calibration metrics frequently disagree on recalibration quality, with ENCE and CWC identified as more consistent performers.