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Forget Unlearning: Towards True Data-Deletion in Machine Learning
Unlearning algorithms aim to remove deleted data's influence from trained models at a cost lower than full retraining.
Knowledge Cross-Distillation for Membership Privacy
A membership inference attack (MIA) poses privacy risks for the training data of a machine learning model.
Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent
What is the information leakage of an iterative randomized learning algorithm about its training data, when the internal state of the algorithm is \emph{private}?
Unifying Ensemble Methods for Q-learning via Social Choice Theory
These methods typically work by employing an aggregation mechanism over actions of different RL algorithms.
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