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Found 6 papers, 1 papers with code
Kernel PCA for multivariate extremes
We propose kernel PCA as a method for analyzing the dependence structure of multivariate extremes and demonstrate that it can be a powerful tool for clustering and dimension reduction.
Differentially private inference via noisy optimization
We propose a general optimization-based framework for computing differentially private M-estimators and a new method for constructing differentially private confidence regions.
Propose, Test, Release: Differentially private estimation with high probability
We derive concentration inequalities for differentially private median and mean estimators building on the "Propose, Test, Release" (PTR) mechanism introduced by Dwork and Lei (2009).
Privacy-preserving parametric inference: a case for robust statistics
Differential privacy is a cryptographically-motivated approach to privacy that has become a very active field of research over the last decade in theoretical computer science and machine learning.
Differentially private sub-Gaussian location estimators
We tackle the problem of estimating a location parameter with differential privacy guarantees and sub-Gaussian deviations.
Centrality measures for graphons: Accounting for uncertainty in networks
Using the theory of linear integral operators, we define degree, eigenvector, Katz and PageRank centrality functions for graphons and establish concentration inequalities demonstrating that graphon centrality functions arise naturally as limits of their counterparts defined on sequences of graphs of increasing size.
