Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions emerge in applications as diverse as hyperparameter tuning, drug discovery, and robotics. BO hinges on a Bayesian surrogate model to sequentially select query points so as to balance exploration with exploitation of the search space. Most existing works rely on a single Gaussian process (GP) based surrogate model, where the kernel function form is typically preselected using domain knowledge. To bypass such a design process, this paper leverages an ensemble (E) of GPs to adaptively select the surrogate model fit on-the-fly, yielding a GP mixture posterior with enhanced expressiveness for the sought funct...
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be...
Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of exper...
This is the author accepted manuscript. The final version is available from ACM via the DOI in this ...
The simulation of complex physics models may lead to enormous computer running times. Since the simu...
Bayesian optimization (BO) based on the Gaussian process (GP) surrogate model has attracted extensiv...
The solution to many science and engineering problems includes identifying the minimum or maximum of...
The solution to many science and engineering problems includes identifying the minimum or maximum of...
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be...
We are concerned primarily with improving the practical applicability of Bayesian optimization. We m...
Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of exper...
This is the author accepted manuscript. The final version is available from ACM via the DOI in this ...
Bayesian optimization has recently emerged in the machine learning community as a very effective aut...
International audienceSurrogate-based optimization is widely used to deal with long-running black-bo...
Surrogate-based optimization is widely used to deal with long-running black-box simulation-based obj...
Optimisation is integral to all sorts of processes in science, economics and arguably underpins the ...
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be...
Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of exper...
This is the author accepted manuscript. The final version is available from ACM via the DOI in this ...
The simulation of complex physics models may lead to enormous computer running times. Since the simu...
Bayesian optimization (BO) based on the Gaussian process (GP) surrogate model has attracted extensiv...
The solution to many science and engineering problems includes identifying the minimum or maximum of...
The solution to many science and engineering problems includes identifying the minimum or maximum of...
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be...
We are concerned primarily with improving the practical applicability of Bayesian optimization. We m...
Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of exper...
This is the author accepted manuscript. The final version is available from ACM via the DOI in this ...
Bayesian optimization has recently emerged in the machine learning community as a very effective aut...
International audienceSurrogate-based optimization is widely used to deal with long-running black-bo...
Surrogate-based optimization is widely used to deal with long-running black-box simulation-based obj...
Optimisation is integral to all sorts of processes in science, economics and arguably underpins the ...
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be...
Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of exper...
This is the author accepted manuscript. The final version is available from ACM via the DOI in this ...