Thesis
Adapted Wasserstein distances and applications to distributionally robust optimization
- Abstract:
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This thesis studies adapted Wasserstein distances and their applications to distributionally robust optimization (DRO) problems in a dynamic context. In Chapter 3, we propose a transfer principle to study the adapted 2-Wasserstein distance between stochastic processes. We obtain an explicit formula for the distance between realvalued mean-square continuous Gaussian processes by introducing causal factorization, an infniite-dimensional analogue of the Cholesky decomposition for operators on Hi...
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- Files:
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(Preview, Dissemination version, pdf, 794.2KB, Terms of use)
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Authors
Contributors
+ Obloj, J
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Mathematical Institute
- Role:
- Supervisor
- ORCID:
- 0000-0002-5686-5498
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/S023925/1
- Programme:
- EPSRC Centre for Doctoral Training in Mathematics of Random Systems: Analysis, Modelling and Simulation
- DOI:
- Type of award:
- DPhil
- Awarding institution:
- University of Oxford
- Language:
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English
- Keywords:
- Pubs id:
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2328939
- Local pid:
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pubs:2328939
- Deposit date:
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2025-10-27
- ARK identifier:
Terms of use
- Copyright holder:
- Yifan Jiang
- Copyright date:
- 2025
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