Differentially Private Hyperparameter Tuning using Local Bayesian Optimization

ArXi:2502.06044v3 Announce Type: replace-cross Hyperparameter tuning is a key component of machine learning procedures, but when validation data contain sensitive user information, search mechanisms can leak private information through the selected configuration. Existing differentially private hyperparameter tuning methods often rely on near-random search, while prior differentially private Bayesian optimization approaches are typically global and, therefore, scale poorly with the hyperparameter dimensionality.