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 …

It has recently been observed that the permanent of a nonnegative square matrix, ie, of a
square matrix containing only nonnegative real entries, can very well be approximated by …

Message-passing algorithms can solve a wide variety of optimization, inference, and
constraint satisfaction problems. The algorithms operate on factor graphs that visually …

The “Divide and Concur”(DC) algorithm introduced by Gravel and Elser can be considered a
competitor to the belief propagation (BP) algorithm, in that both algorithms can be applied to …

In this paper we present a new approach for tightening upper bounds on the partition
function. Our upper bounds are based on fractional covering bounds on the entropy function …

The loopy belief propagation algorithm (LBP) is known to perform extremely well in many
practical problems of probability inference and learning on graphical models, even in …

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 …

Simple, distributed and iterative algorithms, popularly known as message-passing, have
become the architecture of choice for emerging infrastructure networks and the canonical …

The partition function contains valuable information about a graphical model. Unfortunately,
the exact partition function is very often intractable, and so suitable approximations are …

Gaussian belief propagation is an iterative algorithm for computing the mean of a
multivariate Gaussian distribution. Equivalently, the min-sum algorithm can be used to …