Weighted Ensembles for Adaptive Active Learning

Abstract

Labeled data can be expensive to acquire in several application domains, including medical imaging, robotics, computer vision and wireless networks to list a few. To efficiently train machine learning models under such high labeling costs, active learning (AL) judiciously selects the most informative data instances to label on-the-fly. This active sampling process can benefit from a statistical function model, that is typically captured by a Gaussian process (GP) with well-documented merits especially in the regression task. While most GP-based AL approaches rely on a single kernel function, the present contribution advocates an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ensemble</i> of GP (EGP) models with weights adapted to the labeled data collected incrementally. Building on this novel EGP model, a suite of acquisition functions emerges based on the uncertainty and disagreement rules. An adaptively weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ensemble</i> of EGP-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">acquisition functions</i> is advocated to further robustify performance. Extensive tests on synthetic and real datasets in the regression task showcase the merits of the proposed EGP-based approaches with respect to the single GP-based AL alternatives.