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Topics in Causal Inference Addressing Practical Data Challenges
Charlotte Mann
Abstract: Evaluating the effects of interventions is important to inform policy decisions across many disciplines. This dissertation is...
Interpretable Latent Variable Models: Identifiability, Estimation, and Inference
Jing Ouyang
Latent variable models play an increasingly crucial role in modern statistics and machine learning for analyzing large-scale and...
Advances in Machine Learning Safety
Songkai Xue
Abstract: As more and more machine learning (ML) and artificial intelligence (AI) systems are used in practical settings, it is essential...
Bayesian Perspectives on LongROAD Study: Analyzing Driving Decline and Latent Traits
Vincenzo Loffredo
Abstract: The LongROAD study presents several challenges in analyzing and interpreting the data collected. In this thesis, we discuss the...
Regression Methods To Uncover Heterogeneous Effects With Applications To Analyzing Education Disparity
Rebeka Man
Abstract: In today's era of large-scale data, academic institutions, businesses, and government agencies are increasingly faced with...
Statistical Learning and Inference for Network Data via Latent Space Models
Jinming Li
Abstract: With the advancement of technology, network data containing relational information among observations are prevailing across fields...
An Exploration of the Statistical Challenges and Fairness Implications of Transfer Learning
Subha Maity
Abstract: The main goal of transfer learning strategies is to enhance the efficiency of learning models applied to target tasks by...
Statistics in the Modern Era: High Dimensions, Decision-Making, and Privacy
Saptarshi Roy
Abstract: The rapid growth of Artificial Intelligence (AI) and the abundance of data collection from edge devices like cell phones, personal...