7 papers
Bi-Lipschitz Autoencoder With Injectivity Guarantee
Qipeng Zhan, Zhuoping Zhou, Zexuan Wang +2
Autoencoders are widely used for dimensionality reduction, based on the assumption that high-dimensional data lies on low-dimensional manifolds. Regularized autoencoders aim to pre…
Decentralized Online Learning for Random Inverse Problems Over Graphs
Xiwei Zhang, Tao Li, Yan Chen +1
We propose a decentralized online learning algorithm for distributed random inverse problems over network graphs with online measurements, and unifies the distributed parameter est…
Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach
Jiancong Xiao, Bojian Hou, Zhanliang Wang +4
One of the key technologies for the success of Large Language Models (LLMs) is preference alignment. However, a notable side effect of preference alignment is poor calibration: whi…
Fair CCA for Fair Representation Learning: An ADNI Study
Bojian Hou, Zhanliang Wang, Zhuoping Zhou +6
Canonical correlation analysis (CCA) is a technique for finding correlations between different data modalities and learning low-dimensional representations. As fairness becomes cru…
SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text
Weiqing He, Bojian Hou, Tianqi Shang +3
The widespread adoption of large language models (LLMs) has created an urgent need for robust tools to detect LLM-generated text, especially in light of \textit{paraphrasing} techn…
Fairness-Aware Estimation of Graphical Models
Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou +2
This paper examines the issue of fairness in the estimation of graphical models (GMs), particularly Gaussian, Covariance, and Ising models. These models play a vital role in unders…