arXiv (OAI Expanded)
open access
Agentic Search Spaces for Tabular Machine Learning
Despite the rapid progress of LLM-based agents for planning, code generation, and debugging, their practical value for tabular machine learning remains underexplored. In this paper, we investigate a concrete use case: whether state-of-the-art agentic AI systems can design extended HPO search spaces
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