Given samples from an unknown multivariate distribution p, is it possible to distinguish
whether p is the product of its marginals versus p being far from every product distribution …

In this work we present novel differentially private identity (goodness-of-fit) testers for natural
and widely studied classes of multivariate product distributions: Gaussians in R^ d with …

We extend recent higher order concentration results in the discrete setting to include
functions of possibly dependent variables whose distribution (on the product space) satisfies …

We give a nearly-optimal algorithm for testing uniformity of distributions supported on {–1, 1}
n, which makes many queries to a subcube conditional sampling oracle (Bhattacharyya and …

We study the problem of learning Ising models satisfying Dobrushin's condition in the outlier-
robust setting where a constant fraction of the samples are adversarially corrupted. Our main …

Statistical learning theory has largely focused on learning and generalization given
independent and identically distributed (iid) samples. Motivated by applications involving …

In this paper, we study the fluctuations of the average magnetization in an Ising model on an
approximately d N regular graph GN on N vertices. In particular, if GN satisfies a “spectral …

We consider the problem of learning Markov Random Fields (including the prototypical
example, the Ising model) under the constraint of differential privacy. Our learning goals …

We provide improved differentially private algorithms for identity testing of high-dimensional
distributions. Specifically, for $ d $-dimensional Gaussian distributions with known …

We study the problems of learning and testing junta distributions on $\{-1, 1\}^ n $ with
respect to the uniform distribution, where a distribution $ p $ is a $ k $-junta if its probability …