Jul 2, 2022
We give the first polynomial-time, polynomial-sample, differentially private estimator for the mean and covariance of an arbitrary Gaussian distribution N(μ,Σ) in ^d. All previous estimators are either nonconstructive, with unbounded running time, or require the user to specify a priori bounds on the parameters μ and Σ. The primary new technical tool in our algorithm is a new differentially private preconditioner that takes samples from an arbitrary Gaussian N(0,Σ) and returns a matrix A such that A Σ A^T has constant condition number
The conference is held annually since 1988 and has become the leading conference on Learning theory by maintaining a highly selective process for submissions. It is committed in high-quality articles in all theoretical aspects of machine learning and related topics.
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
Presentations on similar topic, category or speaker