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Found 3 papers, 1 papers with code
DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework
In our paper, we introduce DP-HyPO, a pioneering framework for ``adaptive'' private hyperparameter optimization, aiming to bridge the gap between private and non-private hyperparameter optimization.
Federated Linear Contextual Bandits with User-level Differential Privacy
This paper studies federated linear contextual bandits under the notion of user-level differential privacy (DP).
Analytical Composition of Differential Privacy via the Edgeworth Accountant
Many modern machine learning algorithms are composed of simple private algorithms; thus, an increasingly important problem is to efficiently compute the overall privacy loss under composition.
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