AI Synergies


Rémi Delanghe

PhD Candidate

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.


Rémi 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.


Halle Vitree
Day 3 - Nov 8th

Brewery of Ideas

AI Synergies is organized by VUB/ULB, BNVKI and Brewery of Ideas.

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