3 citations · 3 across the 7 of their papers we have counts for
7 papers · 1 filter
Generative Cross-Entropy: A Strictly Proper Loss for Data-Efficient Classification
Qipeng Zhan, Zhuoping Zhou, Li Shen
Cross-entropy (CE) is the default training loss for supervised classification, but its sample efficiency is limited when labels are scarce. Existing remedies primarily act on the d…
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…
PCAE: Learning Ordered Representations in Latent Space for Intrinsic Dimension Estimation via Principal Component Autoencoder
Qipeng Zhan, Zhuoping Zhou, Zexuan Wang +1
Autoencoders have long been considered a nonlinear extension of Principal Component Analysis (PCA). Prior studies have demonstrated that linear autoencoders (LAEs) can recover the…
Multi-Scale Geometric Autoencoder
Qipeng Zhan, Zhuoping Zhou, Zexuan Wang +1
Autoencoders have emerged as powerful models for visualization and dimensionality reduction based on the fundamental assumption that high-dimensional data is generated from a low-d…
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…
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…
Fair Canonical Correlation Analysis
Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou +5
This paper investigates fairness and bias in Canonical Correlation Analysis (CCA), a widely used statistical technique for examining the relationship between two sets of variables.…