Data-driven decisions in stochastic systems : reliability and efficiency
Abstract/Contents
- Abstract
- In Chapters 2-4, we develop approximations for the distribution of regret of multi-armed bandit algorithms. These approximations yield new insights about the exploration-exploitation trade-off in bandit environments. Much of the literature on optimal design of bandit algorithms is based on minimization of expected regret. It is well known that algorithms that are optimal over certain exponential families can achieve expected regret that grows at $\log(T)$ rates with the time horizon $T$, as specified by the Lai-Robbins lower bound. In Chapter 2, we show that when one uses such optimized algorithms, the resulting regret distribution necessarily has a very heavy tail, specifically, that of a truncated Cauchy distribution. Furthermore, for
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Fan, Lin |
|---|---|
| Degree supervisor | Glynn, Peter |
| Thesis advisor | Glynn, Peter |
| Thesis advisor | Blanchet Mancilla, Jose |
| Thesis advisor | Pelger, Markus |
| Degree committee member | Blanchet Mancilla, Jose |
| Degree committee member | Pelger, Markus |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Management Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Lin Fan. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/xk241jp4730 |
Access conditions
- Copyright
- © 2023 by Lin Fan
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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