Publications (14)
Deep Transfer Learning: Model Framework and Error Analysis
Yuling Jiao, Huazhen Lin, Yuchen Luo +1
This paper presents a framework for deep transfer learning, which aims to leverage information from multi-domain upstream data with a large number of samples to a single-domain…
Factor-guided functional PCA for high-dimensional functional data
Shoudao Wen, Huazhen Lin
The literature on high-dimensional functional data focuses on either the dependence over time or the correlation among functional variables. In this paper, we propose a factor-guid…
Adv-SSL: Adversarial Self-Supervised Representation Learning with Theoretical Guarantees
Chenguang Duan, Yuling Jiao, Huazhen Lin +2
Learning transferable data representations from abundant unlabeled data remains a central challenge in machine learning. Although numerous self-supervised learning methods have bee…
Semiparametric regression of mean residual life with censoring and covariate dimension reduction
Ge Zhao, Yanyuan Ma, Huazhen Lin +1
We propose a new class of semiparametric regression models of mean residual life for censored outcome data. The models, which enable us to estimate the expected remaining survival…
Local partial-likelihood estimation for lifetime data
Jianqing Fan, Huazhen Lin, Yong Zhou
This paper considers a proportional hazards model, which allows one to examine the extent to which covariates interact nonlinearly with an exposure variable, for analysis of lifeti…
Beyond the Prompt in Large Language Models: Comprehension, In-Context Learning, and Chain-of-Thought
Yuling Jiao, Yanming Lai, Huazhen Lin +3
Large Language Models (LLMs) have demonstrated remarkable proficiency across diverse tasks, exhibiting emergent properties such as semantic prompt comprehension, In-Context Learnin…