3 papers
stat.ML2020
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification
Antoine Blanchard, Themistoklis Sapsis
We introduce a class of acquisition functions for sample selection that leads to faster convergence in applications related to Bayesian experimental design and uncertainty quantifi…
stat.ML2020
Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and Monitoring
Antoine Blanchard, Themistoklis Sapsis
An unmanned autonomous vehicle (UAV) is sent on a mission to explore and reconstruct an unknown environment from a series of measurements collected by Bayesian optimization. The su…
cs.LG2020
Bayesian Optimization with Output-Weighted Optimal Sampling
Antoine Blanchard, Themistoklis Sapsis
In Bayesian optimization, accounting for the importance of the output relative to the input is a crucial yet challenging exercise, as it can considerably improve the final result b…