2 citations · 2 across the 5 of their papers we have counts for
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Robust and Sparse Generalized Linear Models for High-Dimensional Data via Maximum Mean Discrepancy
Xiaoning Kang, Lulu Kang
High-dimensional datasets are frequently subject to contamination by outliers and heavy-tailed noise, which can severely bias standard regularized estimators like the Lasso. While…
Weighted Average Ensemble for Cholesky-based Covariance Matrix Estimation
Xiaoning Kang, Zhenguo Gao, Xi Liang +1
The modified Cholesky decomposition (MCD) is an efficient technique for estimating a covariance matrix. However, it is known that the MCD technique often requires a pre-specified v…
On Block Cholesky Decomposition for Sparse Inverse Covariance Estimation
Xiaoning Kang, Jiayi Lian, Xinwei Deng
The modified Cholesky decomposition is popular for inverse covariance estimation, but often needs pre-specification on the full information of variable ordering. In this work, we p…
Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing
Xiaoyu Chen, Xiaoning Kang, Ran Jin +1
Fog manufacturing can greatly enhance traditional manufacturing systems through distributed Fog computation units, which are governed by predictive computational workload offloadin…
Multivariate Regression of Mixed Responses for Evaluation of Visualization Designs
Xiaoning Kang, Xiaoyu Chen, Ran Jin +2
Information visualization significantly enhances human perception by graphically representing complex data sets. The variety of visualization designs makes it challenging to effici…