The study of networks leads to a wide range of high-dimensional inference problems. In
many practical applications, one needs to draw inference from one or few large sparse …

We revisit the well-studied problem of efficiently learning the underlying structure and
parameters of an Ising model from data. Current algorithmic approaches achieve essentially …

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

We consider testing and learning problems on causal Bayesian networks as defined by
Pearl (Pearl, 2009). Given a causal Bayesian network M on a graph with n discrete variables …

We study the sample complexity of learning revenue-optimal multi-item auctions. We obtain
the first set of positive results that go beyond the standard but unrealistic setting of item …

We provide finite sample guarantees for the classical Chow-Liu algorithm (IEEE Trans.
Inform. Theory, 1968) to learn a tree-structured graphical model of a distribution. For a …

We study the problem of testing discrete distributions with a focus on the high probability
regime. Specifically, given samples from one or more discrete distributions, a property P …

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 provide time-and sample-efficient algorithms for learning and testing latent-tree Ising
models, ie Ising models that may only be observed at their leaf nodes. On the learning side …

We study the problem of testing* conditional independence* for discrete distributions.
Specifically, given samples from a discrete random variable (X, Y, Z) on domain [ℓ1]×[ℓ2]×[n] …