We consider the problem of estimating the graph associated with a binary Ising Markov
random field. We describe a method based on ℓ 1-regularized logistic regression, in which …

Pauli channels are ubiquitous in quantum information, both as a dominant noise source in
many computing architectures and as a practical model for analyzing error correction and …

We study the problem of learning a local quantum Hamiltonian H given copies of its Gibbs
state ρ= e− β H/(e− β H) at a known inverse temperature β> 0. Anshu, Arunachalam …

We present a scalable nonparametric Bayesian method to perform network reconstruction
from observed functional behavior that at the same time infers the communities present in …

We study the problem of learning a Hamiltonian H to precision ε, supposing we are given
copies of its Gibbs state ρ=\exp(-βH)/Tr(\exp(-βH)) at a known inverse temperature β. Anshu …

graph underlying an Ising model from iid samples. Over the last fifteen years this problem
has been of significant interest in the statistics, machine learning, and statistical physics …

Stochastic networks are a plausible representation of the relational information among
entities in dynamic systems such as living cells or social communities. While there is a rich …

We give a simple, multiplicative-weight update algorithm for learning undirected graphical
models or Markov random fields (MRFs). The approach is new, and for the well-studied case …

We consider the problem of finding the graph on which an epidemic spreads, given only the
times when each node gets infected. While this is a problem of central importance in several …

We investigate a curious relationship between the structure of a discrete graphical model
and the support of the inverse of a generalized covariance matrix. We show that for certain …