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Found 6 papers, 3 papers with code
Differentially Private Covariance Revisited
In this paper, we present two new algorithms for covariance estimation under concentrated differential privacy (zCDP).
OneDConv: Generalized Convolution For Transform-Invariant Representation
Convolutional Neural Networks (CNNs) have exhibited their great power in a variety of vision tasks.
Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming
We study the fundamental problem of frequency estimation under both privacy and communication constraints, where the data is distributed among $k$ parties.
Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation
We consider the problem of estimating the mean of a set of vectors, which are stored in a distributed system.
Communication-Efficient Weighted Sampling and Quantile Summary for GBDT
Gradient boosting decision tree (GBDT) is a powerful and widely-used machine learning model, which has achieved state-of-the-art performance in many academic areas and production environment.
An Information-Theoretic Approach to Detecting Changes in Multi-Dimensional Data Streams
In this paper, we take a general, information-theoretic approach to the change detection problem, which works for multidimensional as well as categorical data.
