35 citations · 53 across the 9 of their papers we have counts for
9 papers
Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction
Xinxing Wu, Chong Peng, Richard Charnigo +1
Interpreting critical variables involved in complex biological processes related to survival time can help understand prediction from survival models, evaluate treatment efficacy,…
Alcohol Intake Differentiates AD and LATE: A Telltale Lifestyle from Two Large-Scale Datasets
Xinxing Wu, Chong Peng, Peter T. Nelson +1
Alzheimer's disease (AD), as a progressive brain disease, affects cognition, memory, and behavior. Similarly, limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recen…
PRIME: Uncovering Circadian Oscillation Patterns and Associations with AD in Untimed Genome-wide Gene Expression across Multiple Brain Regions
Xinxing Wu, Chong Peng, Gregory Jicha +2
The disruption of circadian rhythm is a cardinal symptom for Alzheimer's disease (AD) patients. The full circadian rhythm orchestration of gene expression in the human brain and it…
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization
Chong Peng, Yang Liu, Yongyong Chen +5
In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximatio…
Top- Regularization for Supervised Feature Selection
Xinxing Wu, Qiang Cheng
Feature selection identifies subsets of informative features and reduces dimensions in the original feature space, helping provide insights into data generation or a variety of dom…
Deepened Graph Auto-Encoders Help Stabilize and Enhance Link Prediction
Xinxing Wu, Qiang Cheng
Graph neural networks have been used for a variety of learning tasks, such as link prediction, node classification, and node clustering. Among them, link prediction is a relatively…
Fractal Autoencoders for Feature Selection
Xinxing Wu, Qiang Cheng
Feature selection reduces the dimensionality of data by identifying a subset of the most informative features. In this paper, we propose an innovative framework for unsupervised fe…
Algorithmic Stability and Generalization of an Unsupervised Feature Selection Algorithm
Xinxing Wu, Qiang Cheng
Feature selection, as a vital dimension reduction technique, reduces data dimension by identifying an essential subset of input features, which can facilitate interpretable insight…
Distribution-dependent concentration inequalities for tighter generalization bounds
Xinxing Wu, Junping Zhang
Concentration inequalities are indispensable tools for studying the generalization capacity of learning models. Hoeffding's and McDiarmid's inequalities are commonly used, giving b…