Online Semi-Supervised Learning with Bandit Feedback
arXiv:2010.12574
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.
References in corpus (10)
- Adam: A Method for Stochastic Optimization
- Semi-Supervised Classification with Graph Convolutional Networks
- The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
- Geometric deep learning: going beyond Euclidean data
- A Contextual-Bandit Approach to Personalized News Article Recommendation
- Deep Convolutional Networks on Graph-Structured Data
- Thompson Sampling for Contextual Bandits with Linear Payoffs
- Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL
- Online Semi-Supervised Learning on Quantized Graphs
- Online learning with Corrupted context: Corrupted Contextual Bandits