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.
@article{arxiv.2010.12574,
title = {Online Semi-Supervised Learning with Bandit Feedback},
author = {Sohini Upadhyay and Mikhail Yurochkin and Mayank Agarwal and Yasaman Khazaeni and DjallelBouneffouf},
journal= {arXiv preprint arXiv:2010.12574},
year = {2020}
}