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stat.ML2023★ 1 cited
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models
Jamil Arbas, Hassan Ashtiani, Christopher Liaw
We study the problem of privately estimating the parameters of -dimensional Gaussian Mixture Models (GMMs) with components. For this, we develop a technique to reduce the pr…
stat.ML2021★ 4 cited
Private and polynomial time algorithms for learning Gaussians and beyond
Hassan Ashtiani, Christopher Liaw
We present a fairly general framework for reducing differentially private (DP) statistical estimation to its non-private counterpart. As the main application of…