Online Semi-Supervised Learning with Bandit Feedback

• URL: http://arxiv.org/abs/2010.12574v1
• Date: Fri, 23 Oct 2020 17:56:38 GMT
• Title: Online Semi-Supervised Learning with Bandit Feedback
• Authors: Sohini Upadhyay, Mikhail Yurochkin, Mayank Agarwal, Yasaman Khazaeni and DjallelBouneffouf
• Abstract summary: We formulate a new problem at the intersectionof semi-supervised learning and contextual bandits. We demonstratehow Graph Convolutional Network (GCN), a semi-supervised learning approach, can be adjusted tothe new problem formulation.
• Score: 45.899239661737795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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