Distributionally robust optimization and its applications in mathematical finance, statistics, and reinforcement learning
Abstract/Contents
- Abstract
- Distributionally robust optimization (DRO) is a zero-sum game between a decision-maker and an adversarial player. The decision-maker aims to minimize the expected loss, while the adversarial player wishes the loss to be maximized by replacing the underlying probability measure with another measure within a distributional uncertainty set. DRO has emerged as an important paradigm for machine learning, statistics, and operations research. DRO produces powerful insights in terms of statistical interpretability, performance guarantees, and parameter tuning. In this thesis, we apply DRO to three different topics: martingale optimal transport, convex regression, and offline reinforcement learning. We show how the DRO formulations/techniques improve the existing results in the literature
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Zhou, Zhengqing |
|---|---|
| Degree supervisor | Blanchet, Jose H |
| Degree supervisor | Glynn, Peter W |
| Thesis advisor | Blanchet, Jose H |
| Thesis advisor | Glynn, Peter W |
| Thesis advisor | Papanicolaou, George |
| Degree committee member | Papanicolaou, George |
| Associated with | Stanford University, Department of Mathematics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Zhengqing Zhou |
|---|---|
| Note | Submitted to the Department of Mathematics |
| Thesis | Thesis Ph.D. Stanford University 2021 |
| Location | https://purl.stanford.edu/vn982zg7219 |
Access conditions
- Copyright
- © 2021 by Zhengqing Zhou
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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