Double Linear Thompson Sampling for Context Attentive Bandits
In this paper, we analyze and extend an online learning frame work 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.
ICASSP 2021 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)