We consider the problem of optimizing expensive black-box functions over...
Optimizing expensive-to-evaluate black-box functions of discrete (and
po...
Bayesian optimization (BO) is a sample-efficient approach for tuning des...
Bayesian optimization (BO) is a sample-efficient approach to optimizing
...
This paper explores the environmental impact of the super-linear growth
...
The ability to optimize multiple competing objective functions with high...
Bayesian Optimization is a sample-efficient black-box optimization proce...
When tuning the architecture and hyperparameters of large machine learni...
Optimizing multiple competing black-box objectives is a challenging prob...
Bayesian optimization is a sequential decision making framework for
opti...
In many real-world scenarios, decision makers seek to efficiently optimi...
Bayesian optimization provides sample-efficient global optimization for ...