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Efficient computation of the Knowledge Gradient for Bayesian Optimization
Bayesian optimization is a powerful collection of methods for optimizing stochastic expensive black box functions.
One Step Preference Elicitation in Multi-Objective Bayesian Optimization
We address this issue by using a multi-objective Bayesian optimization algorithm and allowing the DM to select a preferred solution from a predicted continuous Pareto front just once before the end of the algorithm rather than selecting a solution after the end.
Bayesian Optimisation for Constrained Problems
Many real-world optimisation problems such as hyperparameter tuning in machine learning or simulation-based optimisation can be formulated as expensive-to-evaluate black-box functions.
Bayesian Optimisation vs. Input Uncertainty Reduction
Particularly when performing simulation optimisation to find an optimal solution, the uncertainty in the inputs significantly affects the quality of the found solution.
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