A variance maximization criterion for active learning

Active learning aims to train a classifier as fast as possible with as few labels as possible. The core element in virtually any active learning strategy is the criterion that measures the usefulness of the unlabeled data based on which new points to be labeled are picked. We propose a novel approach which we refer to as maximizing variance...

Closed-Loop Calibration of the Interaction Matrix for New Generation Adaptive Optics Systems: A Least Cost Identification Approach

Recently, with the switch to the new generation adaptive optics systems like the VLT and ELT an important identification problem emerged. These telescopes are so-called integrated systems, which means that open-loop calibration of the interaction matrix is not possible anymore. The first and foremost reason for this is that the new generation of...