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SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning
Summary
The source explores a new method for automated machine learning called Tree-Search Enhanced LLM Agents (SELA). SELA uses a large language model (LLM) to suggest potential machine learning strategies, then employs Monte Carlo Tree Search (MCTS) to efficiently explore these options, iteratively refining its approach based on experimental results. This process mimics the nuanced problem-solving approach of human experts and consistently outperforms other AutoML systems and LLM-based agents, particularly in its ability to adapt to diverse datasets and task requirements.
原文链接：https://arxiv.org/abs/2410.17238
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