AdaE-SAEA uses meta-RL to adaptively select infill criteria and ensemble surrogate strategies (bagging/boosting) that balance robustness and accuracy across search phases in SAEAs.
Meta-black-box optimization with bi-space landscape analysis and dual-control mechanism for saea
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TabPFN-3 scales tabular foundation models to 1M rows with synthetic pretraining, test-time compute, and benchmark-leading performance on tabular, relational, and tabular-text tasks while being up to 20x faster than TabPFN-2.5.
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Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA
AdaE-SAEA uses meta-RL to adaptively select infill criteria and ensemble surrogate strategies (bagging/boosting) that balance robustness and accuracy across search phases in SAEAs.
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TabPFN-3: Technical Report
TabPFN-3 scales tabular foundation models to 1M rows with synthetic pretraining, test-time compute, and benchmark-leading performance on tabular, relational, and tabular-text tasks while being up to 20x faster than TabPFN-2.5.