Thompson Sampling on Symmetric Alpha-Stable Bandits
Name
1907.03821.pdf
Description
Accepted version
Size
1.29 MB
Format
Adobe PDF
Checksum (MD5)
45e218cf94b004992c91c1b1f6a40afc
Author(s)
Dubey, Abhimanyu
Pentland, Alex Sandy'
Date Issued
2019
Journal
IJCAI International Joint Conference on Artificial Intelligence
Publisher
International Joint Conferences on Artificial Intelligence
Citation
Dubey, Abhimanyu and Pentland, Alex Sandy'. 2019. "Thompson Sampling on Symmetric Alpha-Stable Bandits." IJCAI International Joint Conference on Artificial Intelligence, 2019-August.
Version
Author's final manuscript
Abstract
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. Thompson Sampling provides an efficient technique to introduce prior knowledge in the multiarmed bandit problem, along with providing remarkable empirical performance. In this paper, we revisit the Thompson Sampling algorithm under rewards drawn from symmetric α-stable distributions, which are a class of heavy-tailed probability distributions utilized in finance and economics, in problems such as modeling stock prices and human behavior. We present an efficient framework for posterior inference, which leads to two algorithms for Thompson Sampling in this setting. We prove finite-time regret bounds for both algorithms, and demonstrate through a series of experiments the stronger performance of Thompson Sampling in this setting. With our results, we provide an exposition of symmetric α-stable distributions in sequential decision-making, and enable sequential Bayesian inference in applications from diverse fields in finance and complex systems that operate on heavy-tailed features.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.24963/IJCAI.2019/792
