My Account Log in

1 option

Transcending Data Boundaries: Transfer Knowledge in Statistical Learning Dongwoo Kim

Dissertations & Theses @ University of Pennsylvania Available online

View online
Format:
Book
Thesis/Dissertation
Author/Creator:
Kim, Dongwoo, author.
Contributor:
University of Pennsylvania. Statistics and Data Science., degree granting institution.
Language:
English
Subjects (All):
Statistics.
Applied mathematics.
Information science.
0463.
0723.
0364.
Local Subjects:
Statistics.
Applied mathematics.
Information science.
0463.
0723.
0364.
Physical Description:
1 electronic resource (122 pages)
Contained In:
Dissertations Abstracts International 86-12A
Place of Publication:
Ann Arbor : ProQuest Dissertations and Theses, 2025
Language Note:
English
Summary:
In contemporary statistical learning, leveraging information across related datasets has emerged as a powerful tool to enhance inference and prediction. This thesis explores the theoretical foundations and practical methodologies of transfer learning within a rigorous statistical framework. We investigate three fundamental problems-functional mean estimation, covariance matrix estimation, and functional linear regression-to elucidate how information from auxiliary domains can be systematically harnessed to improve performance in a primary target domain.Across these problems, the thesis identifies the regimes in which transfer learning outperforms conventional methods and develops adaptive algorithms that are both theoretically sound and practically implementable. By establishing a systematic foundation for transfer learning in statistics, this work advances both the theoretical understanding and the methodological toolkit for transcending traditional data boundaries
Notes:
Source: Dissertations Abstracts International, Volume: 86-12, Section: A.
Advisors: Cai, T. Tony Committee members: Small, Dylan S.; Su, Weijie
Ph.D. University of Pennsylvania 2025
Local Notes:
School code: 0175
ISBN:
9798280757660
Access Restriction:
Restricted for use by site license

The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.

Find

Home Release notes

My Account

Shelf Request an item Bookmarks Fines and fees Settings

Guides

Using the Find catalog Using Articles+ Using your account