55 citations · 89 across the 16 of their papers we have counts for
18 papers
Efficient Agnostic Learning of Univariate Densities Under Local Differential Privacy
Mohammad Afzali, Hassan Ashtiani, Sivan Sabato
We propose a general framework for non-interactive locally differentially private (LDP) learning of structured univariate distributions that can be approximated by piece-wise polyn…
Sample-Efficient Private Learning of Mixtures of Gaussians
Hassan Ashtiani, Mahbod Majid, Shyam Narayanan
We study the problem of learning mixtures of Gaussians with approximate differential privacy. We prove that roughly samples suffice to learn a mix…
Agnostic Private Density Estimation for GMMs via List Global Stability
Mohammad Afzali, Hassan Ashtiani, Christopher Liaw
We consider the problem of private density estimation for mixtures of unrestricted high dimensional Gaussians in the agnostic setting. We prove the first upper bound on the sample…
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples
Mohammad Afzali, Hassan Ashtiani, Christopher Liaw
We study the problem of estimating mixtures of Gaussians under the constraint of differential privacy (DP). Our main result is that s…
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences
Alireza Fathollah Pour, Hassan Ashtiani
We consider the class of noisy multi-layered sigmoid recurrent neural networks with (unbounded) weights for classification of sequences of length , where independent noise d…
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…