We consider the problem of learning a tree-structured Ising model from data, such that
subsequent predictions computed using the model are accurate. Con-cretely, we aim to …

We consider learning Ising tree models when the observations from the nodes are corrupted
by independent but non-identically distributed noise with unknown statistics. Katiyar et …

The k-tensor Ising model is a multivariate exponential family on a p-dimensional binary
hypercube for modeling dependent binary data, where the sufficient statistic consists of all k …

We consider the task of learning Ising models when the signs of different random variables
are flipped independently with possibly unequal, unknown probabilities. In this paper, we …

Defragmentation can potentially be employed as a tactic by perpetrators to conceal,
misrepresent, or eliminate digital evidence. This study explores the effects of minor …

Given side information that an Ising tree-structured graphical model is homogeneous and
has no external field, we derive the exact asymptotics of learning its structure from …

We are interested in the problem of learning the directed acyclic graph (DAG) when data are
generated from a linear structural equation model (SEM) and the causal structure can be …

We consider the problem of learning the underlying structure of a general discrete pairwise
Markov network. Existing approaches that rely on empirical risk minimization may perform …

We consider learning the structures of Gaussian latent tree models with vector observations
when a subset of them are arbitrarily corrupted. First, we present the sample complexities of …

We consider the problem of learning the structure underlying a Gaussian graphical model
when the variables (or subsets thereof) are corrupted by independent noise. A recent line of …