2 papers
stat.ML2025
Gaussian Differential Private Bootstrap by Subsampling
Holger Dette, Carina Graw
Bootstrap is a common tool for quantifying uncertainty in data analysis. However, besides additional computational costs in the application of the bootstrap on massive data, a chal…
stat.ML2024
Uncertainty quantification by block bootstrap for differentially private stochastic gradient descent
Holger Dette, Carina Graw
Stochastic Gradient Descent (SGD) is a widely used tool in machine learning. In the context of Differential Privacy (DP), SGD has been well studied in the last years in which the f…