Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of
expensive black box functions, which use introspective Bayesian models of the function to …

Bayesian optimization (BO) is a successful methodology to optimize black-box functions that
are expensive to evaluate. While traditional methods optimize each black-box function in …

Bayesian optimization has recently emerged as a powerful and flexible tool in machine
learning for hyperparameter tuning and more generally for the efficient global optimization of …

Bayesian optimization is a powerful approach for the global derivative-free optimization of
non-convex expensive functions. Even though there is a rich literature on Bayesian …

Bayesian optimization (BO) has become an established framework and popular tool for
hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its …

Bayesian optimization (BO) has recently emerged as a powerful and flexible tool for hyper-
parameter tuning and more generally for the efficient global optimization of expensive black …

Bayesian optimization is a prominent method for optimizing expensive to evaluate black-box
functions that is prominently applied to tuning the hyperparameters of machine learning …

Many modern machine learning algorithms have a large number of hyperparameters. To
effectively use these algorithms, we need to pick good hyperparameter values. In this article …

Bayesian optimization (BO) is a model-based approach for gradient-free black-box function
optimization, such as hyperparameter optimization. Typically, BO relies on conventional …

Computational models in fields such as computational neuroscience are often evaluated via
stochastic simulation or numerical approximation. Fitting these models implies a difficult …