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Robust sequential decision-making on networks

Author(s)
Dubey, Abhimanyu.
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Other Contributors
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Advisor
Alex P. Pentland.
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MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
In this thesis, I consider the research problem of designing optimal algorithms for two specific settings of the stochastic multi-armed bandit problem. The first setting considers the problem where rewards are drawn from a family of extremely heavy-tailed distributions known as a-stable distributions. For this setting, I extended an existing upper confidence bound algorithm, to create an optimal frequentist algorithm, titled [alpha]-UCB. Next, I developed a variant of the Bayesian Thompson Sampling algorithm in this setting, titled Robust [alpha]-TS, which involved developing an efficient pipeline for posterior inference. I also proved finite-time regret bounds for this algorithm, that are optimal up to logarithmic factors. The second problem setting I considered was the networked multi-agent problem where agents have local communication, and have unique preferences. This problem setting is a generalization of the co-operative multi-agent stochastic bandit problem, and is a closely related variant of the single-agent bandit setting with side observations. For this setting, I developed an optimal upper confidence bound algorithm, titled Net-UCB. I also proved finite-time regret bounds for this algorithm that are logarithmic in the number of rounds, and are sub-linear in the number of agents. For both settings, I conducted extensive experiments to verify the tightness of the regret bounds established, and compare performance with existing state-of-the-art algorithms. The algorithms proposed in this thesis obtain competitive regret and state-of-the-art performance across a variety of problem settings.
Description
Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2019
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (pages 99-106).
 
Date issued
2019
URI
https://hdl.handle.net/1721.1/123636
Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Publisher
Massachusetts Institute of Technology
Keywords
Program in Media Arts and Sciences

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