Continuous Exploitative Measurement Trajectories Using Bayesian Optimization
Line-based sampling strategies aim to capture as
much information as possible along a trajectory, whilst minimizing the
trajectory's length. The current state of the art primarily contains
exploration techniques that focus on uniformly sampling the measurement space.
In this work, Bayesian optimization is used to create a novel exploitative
line-based sampling strategy, that is able to guide the sampling process
towards interesting regions.
Delanghe received his M. Sc. degree in Computer Science Engineering from Ghent
University in 2019. Starting from September 2019, he is active as a PhD student
in the Internet Technology and Data Science Lab (IDLab) at Ghent University
where he is working on machine learning techniques and data-analysis tools for
line-based design of experiments and industrial optimization.