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Found 8 papers, 2 papers with code
Optimal Differential Privacy Composition for Exponential Mechanisms
We consider precise composition bounds of the overall privacy loss for exponential mechanisms, one of the fundamental classes of mechanisms in DP.
Instance-Specific Asymmetric Sensitivity in Differential Privacy
We provide a new algorithmic framework for differentially private estimation of general functions that adapts to the hardness of the underlying dataset.
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance.
Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization
To that end, this paper presents a thorough empirical evaluation of mSAM on various tasks and datasets.
Heterogeneous Calibration: A post-hoc model-agnostic framework for improved generalization
We introduce the notion of heterogeneous calibration that applies a post-hoc model-agnostic transformation to model outputs for improving AUC performance on binary classification tasks.
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition
We design algorithms that ensures (approximate) differential privacy and only needs access to the true top-k' elements from the data for any chosen k' ≥ k. This is a highly desirable feature for making differential privacy practical, since the algorithms require no knowledge of the domain.
Practical Differentially Private Top-$k$ Selection with Pay-what-you-get Composition
We study the problem of top-$k$ selection over a large domain universe subject to user-level differential privacy.
Cryptography and Security
Efficient Second-Order Shape-Constrained Function Fitting
We give an algorithm to compute a one-dimensional shape-constrained function that best fits given data in weighted-$L_{\infty}$ norm.
