Louis C. Tiao

Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch

This work introduces agentic Bayesian optimization: a paradigm in which an LLM agent is the central decision maker in the BO loop while a Bayesian backend provides the uncertainty-aware optimization substrate, and demonstrates the practical value of agentic BO in dynamic settings.