2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDMLSP-25.4
Paper Title DOUBLE-LINEAR THOMPSON SAMPLING FOR CONTEXT-ATTENTIVE BANDITS
Authors Djallel Bouneffouf, IBM Research, United States; Raphael Feraud, Orange, France; Sohini Upadhyay, IBM Research, United States; Yasaman Khazaeni, Irina Rish, Universite de montreal, United States
SessionMLSP-25: Reinforcement Learning 1
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-REI] Reinforcement learning
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
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.