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arXiv · 2010.09473

Double-Linear Thompson Sampling for Context-Attentive Bandits

Djallel Bouneffouf·Raphaël Féraud·Sohini Upadhyay·Yasaman Khazaeni·Irina Rish
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Abstract

In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration;however, the agent has a freedom to choose which variables to observe. We derive a novel algorithm, called Context-Attentive Thompson Sampling (CATS), which builds upon the Linear Thompson Sampling approach, adapting it to Context-Attentive Bandit setting. We provide a theoretical regret analysis and an extensive empirical evaluation demonstrating advantages of the proposed approach over several baseline methods on a variety of real-life datasets

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Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay, Yasaman Khazaeni, Irina Rish. 2020-10-15. Double-Linear Thompson Sampling for Context-Attentive Bandits. https://arxiv.org/abs/2010.09473

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