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This study suggests that LLM agents can provide practical value for tabular ML by expanding the design space, and suggests that the two strongest agentic ensembles surpass the best AutoGluon ensemble of conventional models.
This work introduces TabPack, an efficient MLP ensemble with strong out-of-the-box performance and reduced reliance on traditional tuning, and specifies ranges from which to sample MLP hyperparameter rather than exact hyperparameter values, which naturally demands less precision for good performance.
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