Double-Linear Thompson Sampling for Context-Attentive Bandits

• URL: http://arxiv.org/abs/2010.09473v1
• Date: Thu, 15 Oct 2020 13:01:19 GMT
• Title: Double-Linear Thompson Sampling for Context-Attentive Bandits
• Authors: Djallel Bouneffouf, Rapha\"el F\'eraud, Sohini Upadhyay, Yasaman Khazaeni and Irina Rish
• Abstract summary: We analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications. 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.
• Score: 27.786695164493562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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