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stat.ML2023
DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning
Spencer Giddens, Fang Liu
Differential privacy (DP) is the state-of-the-art framework for guaranteeing privacy for individuals when releasing aggregated statistics or building statistical/machine learning m…
stat.ML2023
A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning
Spencer Giddens, Yiwang Zhou, Kevin R. Krull +3
Data used to train predictive models via empirical risk minimization (ERM) often contain sensitive personal information. While differential privacy (DP) provides mathematically pro…
stat.ML2022★ 1 cited
Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models
Yinan Li, Fang Liu
We propose the AdaPtive Noise Augmentation (PANDA) procedure to regularize the estimation and inference of generalized linear models (GLMs). PANDA iteratively optimizes the objecti…