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Found 4 papers, 1 papers with code
PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
We introduce a privacy auditing scheme for ML models that relies on membership inference attacks using generated data as "non-members".
DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)
The Adam optimizer is a popular choice in contemporary deep learning, due to its strong empirical performance.
DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation
We observe that the traditional use of DP with the Adam optimizer introduces a bias in the second moment estimation, due to the addition of independent noise in the gradient computation.
Learning to Solve Soft-Constrained Vehicle Routing Problems with Lagrangian Relaxation
However, using learning-based approaches to address more types of constrained VRP remains a challenge.
