Derives a generalization bound for GP-based symbolic regression that decomposes the gap into structure-selection complexity and constant-fitting complexity under tree constraints.
Proceedings of the national academy of sciences113(15), 3932–3937 (2016)
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UBD creates a universal space for brain dynamics that predicts fMRI signals with Pearson's r greater than 0.9 across eight states and 963 subjects, revealing mechanisms of cognitive transitions and individual differences.
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On the Generalization Bounds of Symbolic Regression with Genetic Programming
Derives a generalization bound for GP-based symbolic regression that decomposes the gap into structure-selection complexity and constant-fitting complexity under tree constraints.
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A Universal Space of Brain Dynamics for Unveiling Cognitive Transitions and Individual Differences
UBD creates a universal space for brain dynamics that predicts fMRI signals with Pearson's r greater than 0.9 across eight states and 963 subjects, revealing mechanisms of cognitive transitions and individual differences.