Online Semi-Supervised Learning with Bandit Feedback
This addresses the challenge of online decision-making with limited labeled data for domains such as healthcare and advertising, though it appears incremental.
The paper tackles the problem of combining semi-supervised learning with contextual bandits for applications like clinical trials and ad recommendations, resulting in algorithms verified on real-world datasets.
We formulate a new problem at the intersectionof semi-supervised learning and contextual bandits,motivated by several applications including clini-cal trials and ad recommendations. We demonstratehow Graph Convolutional Network (GCN), a semi-supervised learning approach, can be adjusted tothe new problem formulation. We also propose avariant of the linear contextual bandit with semi-supervised missing rewards imputation. We thentake the best of both approaches to develop multi-GCN embedded contextual bandit. Our algorithmsare verified on several real world datasets.