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Learning from Interactions via Online Decision-making and Network Science

File(s)
Kumar_cornellgrad_0058F_14515.pdf (1.4 MB)
Permanent Link(s)
https://doi.org/10.7298/q5x3-cb41
https://hdl.handle.net/1813/116495
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Cornell Theses and Dissertations
Author
Kumar, Raunak
Abstract

Interactions between a learner and an environment arise in a variety of domains, ranging from online recommendations (e.g., Spotify) to control of physical dynamical systems (e.g., temperature regulation in a datacenter). In this dissertation we study such problems through two different perspectives: online decision-making and network science. In Part I we study how a learner should make decisions

Description
257 pages
Date Issued
2024-08
Keywords
bandits
•
network science
•
online control
•
online convex optimization
•
online learning
•
triangles
Committee Chair
Kleinberg, Robert
Committee Member
Dean, Sarah
Sridharan, Karthik
Tardos, Eva
Degree Discipline
Computer Science
Degree Name
Ph. D., Computer Science
Degree Level
Doctor of Philosophy
Rights
Attribution-NonCommercial 4.0 International
Rights URI
https://creativecommons.org/licenses/by-nc/4.0/
Type
dissertation or thesis
Link(s) to Catalog Record
https://newcatalog.library.cornell.edu/catalog/16611960

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