No-Regret Bayesian Optimization with Unknown Hyperparameters

JOURNAL OF MACHINE LEARNING RESEARCH, 2019.

Cited by: 13|Views26
EI

Abstract:

Bayesian optimization (BO) based on Gaussian process models is a powerful paradigm to optimize black-box functions that are expensive to evaluate. While several BO algorithms provably converge to the global optimum of the unknown function, they assume that the hyperparameters of the kernel are known in advance. This is not the case in pra...More

Code:

Data:

Your rating :
0

 

Tags
Comments