Thesis
Mirror descent for high-dimensional statistical models
- Abstract:
-
As vast amounts of data are being produced and processed in various fields of science and engineering, optimizing high-dimensional statistical models has become a ubiquitous task in many modern applications. Often, it is crucial to exploit underlying low-dimensional structures to extract useful information from high-dimensional data and models. To this end, numerous optimization algorithms such as gradient descent and its variants have been extensively studied both empirically and theoreti...
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Authors
Contributors
+ Rebeschini, P
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Statistics
- Sub department:
- Statistics
- Oxford college:
- University College
- Role:
- Supervisor
- ORCID:
- 0000-0001-7772-4160
+ Engineering and Physical Sciences Research Council and Medical Research Council Centre for Doctoral Training in Next Generation Statistical Science
More from this funder
- Grant:
- EP/L016710/1
- Programme:
- The Oxford-Warwick Statistics Programme
- DOI:
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
- Language:
-
English
- Keywords:
- Subjects:
- Deposit date:
-
2022-04-09
Terms of use
- Copyright holder:
- Wu, F
- Copyright date:
- 2021
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