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May the Noise be with you: Adversarial Training without Adversarial Examples
We model the propagation of noise through the layers, introducing a closed-form stochastic loss function that encapsulates a noise variance parameter.
Exploring Machine Learning Privacy/Utility trade-off from a hyperparameters Lens
Towards investigating new ground for better privacy-utility trade-off, this work questions; (i) if models' hyperparameters have any inherent impact on ML models' privacy-preserving properties, and (ii) if models' hyperparameters have any impact on the privacy/utility trade-off of differentially private models.
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