2020/10/23 by Sohini Upadhyay, Mikhail Yurochkin, Upadhyay, Sohini +6 · 1 citation
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Data Stream Mining Techniques #Machine Learning and Algorithms #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2010.12574
arxiv created 2020/10/23 · arxiv updated 2020/10/26
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.