Polynomial time and private learning of unbounded gaussian mixture models
We study the problem of privately estimating the parameters of $ d $-dimensional Gaussian
Mixture Models (GMMs) with $ k $ components. For this, we develop a technique to reduce …
Mixture Models (GMMs) with $ k $ components. For this, we develop a technique to reduce …
Clustering mixtures with almost optimal separation in polynomial time
We consider the problem of clustering mixtures of mean-separated Gaussians in high
dimensions. We are given samples from a mixture of k identity covariance Gaussians, so that …
dimensions. We are given samples from a mixture of k identity covariance Gaussians, so that …
Mixtures of gaussians are privately learnable with a polynomial number of samples
We study the problem of estimating mixtures of Gaussians under the constraint of differential
privacy (DP). Our main result is that $\tilde {O}(k^ 2 d^ 4\log (1/\delta)/\alpha^ 2\varepsilon) …
privacy (DP). Our main result is that $\tilde {O}(k^ 2 d^ 4\log (1/\delta)/\alpha^ 2\varepsilon) …
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples
We study the problem of estimating mixtures of Gaussians under the constraint of differential
privacy (DP). Our main result is that $\text {poly}(k, d, 1/\alpha, 1/\varepsilon,\log (1/\delta)) …
privacy (DP). Our main result is that $\text {poly}(k, d, 1/\alpha, 1/\varepsilon,\log (1/\delta)) …
PRIVATE DENSITY ESTIMATION FOR MIXTURE DISTRIBUTIONS AND GAUSSIAN MIXTURE MODELS
M Afzali Kharkouei - 2024 - macsphere.mcmaster.ca
We develop a general technique for estimating (mixture) distributions under the constraint of
differential privacy (DP). On a high level, we show that if a class of distributions (such as …
differential privacy (DP). On a high level, we show that if a class of distributions (such as …