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Statistical gems in ai toward reliable and efficient intelligence Yangxinyu Xie
- Format:
- Book
- Thesis/Dissertation
- Author/Creator:
- Xie, Yangxinyu, author.
- Language:
- English
- Subjects (All):
- Statistics.
- Computer engineering.
- Information technology.
- 0463.
- 0489.
- 0464.
- 0800.
- Local Subjects:
- Statistics.
- Computer engineering.
- Information technology.
- 0463.
- 0489.
- 0464.
- 0800.
- Genre:
- Academic theses
- Physical Description:
- 1 online resource (217 pages)
- Contained In:
- Dissertations Abstracts International 87-12B
- Place of Publication:
- Ann Arbor : ProQuest Dissertations and Theses, 2026
- Language Note:
- English
- Summary:
- The rapid rise of large language models (LLMs) has reshaped how humans create, reason, and communicate, yet their reliability and efficiency remain imperfectly understood. This dissertation develops statistical tools to evaluate and enhance these systems. We demonstrate how hypothesis testing, coupling methods, and conformal inference can ensure that advanced artificial intelligence (AI) models operate with safety, fairness, and efficiency. First, we design hypothesis-testing frameworks that distinguish genuine reasoning from token bias, revealing fundamental limits in LLMs' ability to generalize logically. Next, we introduce statistically principled watermarking methods for detecting AI-generated content and safeguarding digital integrity. Building on this, we explore the application of conformal inference to ensure fair and robust detection of excessive AI use in classrooms while embracing the evolving world of human-AI collaboration.Finally, we propose statistical early stopping rules that enhance generative efficiency while maintaining accuracy. Together, these studies uncover key "statistical gems" that bridge AI technology with trustworthy and efficient real-world deployment
- Notes:
- Source: Dissertations Abstracts International, Volume: 87-12, Section: B.
- Advisors: Su, Weijie Committee members: Zhao, Linda H.; Ren, Zhimei
- Ph.D. University of Pennsylvania 2026
- Vendor supplied data
- Local Notes:
- School code: 0175
- ISBN:
- 9798247980063
- Access Restriction:
- Restricted for use by site license
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