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Multi-Task Learning for Contextual Bandits
[article]
2017
arXiv
pre-print
Contextual bandits are a form of multi-armed bandit in which the agent has access to predictive side information (known as the context) for each arm at each time step, and have been used to model personalized news recommendation, ad placement, and other applications. In this work, we propose a multi-task learning framework for contextual bandit problems. Like multi-task learning in the batch setting, the goal is to leverage similarities in contexts for different arms so as to improve the
arXiv:1705.08618v1
fatcat:p4v5iss7uvcvrnt7t2khry3anq