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 …
Settling the robust learnability of mixtures of gaussians
This work represents a natural coalescence of two important lines of work–learning mixtures
of Gaussians and algorithmic robust statistics. In particular we give the first provably robust …
of Gaussians and algorithmic robust statistics. In particular we give the first provably robust …
Minimax rates for robust community detection
In this work, we study the problem of community detection in the stochastic block model with
adversarial node corruptions. Our main result is an efficient algorithm that can tolerate an ϵ …
adversarial node corruptions. Our main result is an efficient algorithm that can tolerate an ϵ …
Distribution learnability and robustness
We examine the relationship between learnability and robust learnability for the problem of
distribution learning. We show that learnability implies robust learnability if the adversary …
distribution learning. We show that learnability implies robust learnability if the adversary …
Tensor decompositions meet control theory: learning general mixtures of linear dynamical systems
Abstract Recently Chen and Poor initiated the study of learning mixtures of linear dynamical
systems. While linear dynamical systems already have wide-ranging applications in …
systems. While linear dynamical systems already have wide-ranging applications in …
Robustly learning general mixtures of gaussians
This work represents a natural coalescence of two important lines of work—learning
mixtures of Gaussians and algorithmic robust statistics. In particular, we give the first …
mixtures of Gaussians and algorithmic robust statistics. In particular, we give the first …
Mixtures of gaussians are privately learnable with a polynomial number of samples
Robust voting rules from algorithmic robust statistics
Maximum likelihood estimation furnishes powerful insights into voting theory, and the design
of voting rules. However the MLE can usually be badly corrupted by a single outlying …
of voting rules. However the MLE can usually be badly corrupted by a single outlying …