We consider the problem of optimizing expensive black-box functions over...
Bayesian optimization (BO) is a popular approach for sample-efficient
op...
Optimizing expensive-to-evaluate black-box functions of discrete (and
po...
Bayesian optimization (BO) is a powerful approach to sample-efficient
op...
The ability to optimize multiple competing objective functions with high...
When tuning the architecture and hyperparameters of large machine learni...
Bayesian optimization (BO) is a popular method for optimizing
expensive-...
This paper presents the results and insights from the black-box optimiza...
Bayesian optimization (BO) is a powerful paradigm for efficient optimiza...
Matrix square roots and their inverses arise frequently in machine learn...
Bayesian optimization (BO) is a class of sample-efficient global optimiz...
The global optimization of a high-dimensional black-box function under
b...
Bayesian optimization has recently emerged as a popular method for the
s...
This paper describes Plumbing for Optimization with Asynchronous Paralle...
Gaussian processes (GPs) with derivatives are useful in many application...
For applications as varied as Bayesian neural networks, determinantal po...