143 citations · 381 across the 13 of their papers we have counts for
3 papers · 1 filter
Bayesian Optimization Allowing for Common Random Numbers
Michael Pearce, Matthias Poloczek, Juergen Branke
Bayesian optimization is a powerful tool for expensive stochastic black-box optimization problems such as simulation-based optimization or machine learning hyperparameter tuning. M…
Dynamic Subgoal-based Exploration via Bayesian Optimization
Yijia Wang, Matthias Poloczek, Daniel R. Jiang
Reinforcement learning in sparse-reward navigation environments with expensive and limited interactions is challenging and poses a need for effective exploration. Motivated by comp…
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…