143 citations · 150 across the 4 of their papers we have counts for
3 papers · 1 filter
Efficient computation of the Knowledge Gradient for Bayesian Optimization
Juan Ungredda, Michael Pearce, Juergen Branke
Bayesian optimization is a powerful collection of methods for optimizing stochastic expensive black box functions. One key component of a Bayesian optimization algorithm is the acq…
Bayesian Optimisation vs. Input Uncertainty Reduction
Juan Ungredda, Michael Pearce, Juergen Branke
Simulators often require calibration inputs estimated from real world data and the quality of the estimate can significantly affect simulation output. Particularly when performing…
Scalable Global Optimization via Local Bayesian Optimization
David Eriksson, Michael Pearce, Jacob R Gardner +2
Bayesian optimization has recently emerged as a popular method for the sample-efficient optimization of expensive black-box functions. However, the application to high-dimensional…