# Online Semi-Supervised Learning with Bandit Feedback

arXiv: https://arxiv.org/abs/2010.12574 · HTML: https://mlsanity.com/paper/2010.12574/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Neural Bandit with Arm Group Graph](https://mlsanity.com/paper/2206.03644/) (2022)
- [Etat de l'art sur l'application des bandits multi-bras](https://mlsanity.com/paper/2101.00001/) (2021)

## Abstract (arXiv)

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.