3 papers
cs.LG2025
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…
stat.ML2023
Knowledge Gradient for Multi-Objective Bayesian Optimization with Decoupled Evaluations
Jack M. Buckingham, Sebastian Rojas Gonzalez, Juergen Branke
Multi-objective Bayesian optimization aims to find the Pareto front of trade-offs between a set of expensive objectives while collecting as few samples as possible. In some cases,…
cs.LG2022
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…