Events

PhD Defense Learning to Adapt: From Structured Domain Adaptation to Efficient Inference for Large Language Models
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Speaker: Zihao Xu

Bio

Location : CoRE 301

Committee: 

Assistant Professor Hao Wang

Professor Chengzhi Mao

Professor Ruixiang Tang

Prof. Yuzhe Yang (external)

Event Type: PhD Defense

Abstract: Adaptation is a fundamental challenge in machine learning: models must remain effective under domain shift, changing task requirements, and limited computational budgets. We study adaptation as a unifying principle across two settings: structured domain adaptation and efficient inference for large language models. First, it develops methods that move beyond uniform domain alignment by modeling relationships among domains through graphs, taxonomies or latent domain indices, enabling more effective transfer under complex distribution shifts. Second, it extends adaptation to inference in large language models, introducing methods that compress or implicitly encode task context to reduce inference cost while maintaining strong performance. Together, these contributions present a broader view of adaptation, showing how machine learning systems can be designed to operate effectively under structural, informational, and computational constraints.

Organization: 

Contact  Assistant Professor Hao Wang

Zoom Link: https://rutgers.zoom.us/j/9557767148?pwd=aE1iQURKc3RGYW50VjA3QWVsNkRRQT09