4 citations · 7 across the 5 of their papers we have counts for
5 papers
Bayesian Optimisation: Which Constraints Matter?
Xietao Wang Lin, Juan Ungredda, Max Butler +4
Bayesian optimisation has proven to be a powerful tool for expensive global black-box optimisation problems. In this paper, we propose new Bayesian optimisation variants of the pop…
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…
One Step Preference Elicitation in Multi-Objective Bayesian Optimization
Juan Ungredda, Mariapia Marchi, Teresa Montrone +1
We consider a multi-objective optimization problem with objective functions that are expensive to evaluate. The decision maker (DM) has unknown preferences, and so the standard app…
Bayesian Optimisation for Constrained Problems
Juan Ungredda, Juergen Branke
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 functi…
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…