About this document
Bayesian Optimization with Unknown Hyperparameters by surajdey536 is a document available to read on EtoBox.
This document presents a theoretical analysis of Bayesian optimization when the hyperparameters of the Gaussian process model are unknown. It addresses challenges related to uncertain hyperparameters, including their effects on convergence, regret bounds, and acquisition function performance. The study aims to enhance the understanding of optimization behavior in the presence of model uncertainty.
- Author
- surajdey536
- Language
- EN