We propose a general methodology for the construction and analysis of essentially minimax
estimators for a wide class of functionals of finite dimensional parameters, and elaborate on …

We consider the problem of high-dimensional Ising (graphical) model selection. We propose
a simple algorithm for structure estimation based on the thresholding of the empirical …

Consider a data publishing setting for a dataset composed by both private and non-private
features. The publisher uses an empirical distribution, estimated from n iid samples, to …

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 consider the problem of high-dimensional Gaussian graphical model selection. We
identify a set of graphs for which an efficient estimation algorithm exists, and this algorithm is …

We study graph estimation and density estimation in high dimensions, using a family of
density estimators based on forest structured undirected graphical models. For density …

Hypothesis testing is a statistical inference framework for determining the true distribution
among a set of possible distributions for a given data set. Privacy restrictions may require the …

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 learning a tree Ising model from samples such that subsequent
predictions made using the model are accurate. The prediction task considered in this paper …

The conventional channel resolvability problem refers to the determination of the minimum
rate required for an input process so that the output distribution approximates a target …