Search Results for author:
Found 4 papers, 2 papers with code
MISNN: Multiple Imputation via Semi-parametric Neural Networks
Multiple imputation (MI) has been widely applied to missing value problems in biomedical, social and econometric research, in order to avoid improper inference in the downstream data analysis.
Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data
Single imputation methods such as matrix completion methods do not adequately account for imputation uncertainty and hence would yield improper statistical inference.
Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems
Missing data are present in most real world problems and need careful handling to preserve the prediction accuracy and statistical consistency in the downstream analysis.
On the Convergence and Calibration of Deep Learning with Differential Privacy
Differentially private (DP) training preserves the data privacy usually at the cost of slower convergence (and thus lower accuracy), as well as more severe mis-calibration than its non-private counterpart.
