5 papers
Learning Gaussian Graphical Models from a Glauber Trajectory Without Mixing
Eric Shen, Tony Wu, Mahbod Majid +1
We study the task of learning the structure of a -sparse Gaussian graphical model on variables from a single trajectory of Glauber dynamics. Beyond algorithmic consideration…
Privately Estimating Monotone Statistics in Polynomial Time
Gavin Brown, Ephraim Linder, Mahbod Majid +1
We study efficient differentially private algorithms for estimating monotone statistics, i.e., statistics that are monotone under the addition of new observations. The starting poi…
Computation-Utility-Privacy Tradeoffs in Bayesian Estimation
Sitan Chen, Jingqiu Ding, Mahbod Majid +1
Bayesian methods lie at the heart of modern data science and provide a powerful scaffolding for estimation in data-constrained settings and principled quantification and propagatio…
Sample-Optimal Private Regression in Polynomial Time
Prashanti Anderson, Ainesh Bakshi, Mahbod Majid +1
We consider the task of privately obtaining prediction error guarantees in ordinary least-squares regression problems with Gaussian covariates (with unknown covariance structure).…
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…