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arXiv Machine Learning By Tejus Gupta, Efe Mert Karag\"ozl\"u, Rohit Sonker, Barnab\'as P\'oczos, Jeff Schnieder

Bayesian Optimization with Rich Auxiliary Information via LLMs

Read the original on arXiv Machine Learning →

The paper introduces Bayesian Optimization (BO) techniques that incorporate rich auxiliary information—such as training curves, expert notes, images, and prior knowledge—using large language models (LLMs). Three new methods are proposed to integrate this auxiliary data into BO, and they are evaluated on hyperparameter optimization benchmarks and a real-world nuclear fusion task. The results show that these LLM-enhanced BO methods consistently outperform standard BO and existing LLM-based optimization approaches.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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