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A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning
While these models can be highly accurate in prediction, results obtained from these models with the use of sensitive data may be susceptible to privacy attacks.
Robust High-Dimensional Regression with Coefficient Thresholding and its Application to Imaging Data Analysis
It is of importance to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible outliers in real-world applications such as imaging data analyses.
Data Discovery Using Lossless Compression-Based Sparse Representation
Sparse representation has been widely used in data compression, signal and image denoising, dimensionality reduction and computer vision.
Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes
$\textbf{Conclusion}$: The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring.
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