Bayesian Optimization with Rich Auxiliary Information via LLMs
Bayesian Optimization (BO) is widely used for optimizing expensive black-box functions, yet it typically reduces each expensive experiment to an input and its objective value, discarding much of the information the experiment produces. Such auxiliary information can include training curves in hyperparameter optimization, expert notes and images in scientific experimentation, or known facts about the specific optimization problem. Leveraging the ability of large language models (LLMs) to process diverse and unstructured information, we introduce two methods: AuxBO-Evolve, which uses auxiliary information to evolve beliefs over the location of the optimum, and AuxBO-Vicinity, which uses it ...
| Authors | Efe Mert Karagözlü, Rohit Sonker, Tejus Gupta et al. |
|---|---|
| Primary category | cs.LG |
| Published | 16 September 2026 |
| Last updated at source | 2 October 2026 |
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