EMO-STA evolves a shared program archive across task families then adapts candidates to targets, outperforming matched-compute single-task evolution in most of eight families while reducing overfitting on low-data tasks like ARC.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
Presents first online L2D algorithm for multiclass classification with bandit feedback and varying experts, achieving O((n+n_e)T^{2/3}) regret generally and O((n+n_e)√T) under low noise.
A barrier-enforced multi-objective optimization framework for neural networks generates sharp non-crossing prediction intervals that meet exact target coverage in probabilistic forecasting.
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