6 papers
Membership Inference via Pairwise Likelihood Ratios
Shengjie Niu, Zebin Yun, Yeheng Ge +1
Membership inference attacks (MIAs) are the standard tool for auditing the privacy risks of machine learning models. Given a query point, an MIA aims to determine whether that poin…
Black-Box Membership Inference via Word-Level Probability Estimation
Shengjie Niu, Yeheng Ge, Jian Huang
Membership inference attacks (MIAs) have emerged as critical tools for auditing privacy risks in large language models (LLMs), aiming to determine whether a given text was included…
Learning Guarantee of Reward Modeling Using Deep Neural Networks
Yuanhang Luo, Yeheng Ge, Ruijian Han +1
In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks. We establish a novel non-asymptotic regret bound for deep re…
Treatment Effects Inference with High-Dimensional Instruments and Control Variables
Xiduo Chen, Xingdong Feng, Antonio F. Galvao +1
Obtaining valid treatment effect inference remains a challenging problem when dealing with numerous instruments and non-sparse control variables. In this paper, we propose a novel…
Transfer Learning through Enhanced Sufficient Representation: Enriching Source Domain Knowledge with Target Data
Yeheng Ge, Xueyu Zhou, Jian Huang
Transfer learning is an important approach for addressing the challenges posed by limited data availability in various applications. It accomplishes this by transferring knowledge…
Structure learning via unstructured kernel-based M-regression
Xin He, Yeheng Ge, Xingdong Feng
In statistical learning, identifying underlying structures of true target functions based on observed data plays a crucial role to facilitate subsequent modeling and analysis. Unli…