Identifying latent variables and causal structures from observational data is essential to
many real-world applications involving biological data, medical data, and unstructured data …

Real‐world applications often involve multifaceted data with several reasonable
interpretations. To cluster this data, we need methods that are able to produce multiple …

Probabilistic context-free grammars (PCFGs) and dynamic Bayesian networks (DBNs) are
widely used sequence models with complementary strengths and limitations. While PCFGs …

We consider the problem of structure recovery in a graphical model of a tree where some
variables are latent. Specifically, we focus on the Gaussian case, which can be reformulated …

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 …

In the analysis of real-world data, it is useful to learn a latent variable model that represents
the data generation process. In this setting, latent tree models are useful because they are …

This survey provides an overview of common applications, both implicit and explicit, of"
tensors" and" tensor products" in the fields of data science and statistics. One goal is to …

Identifying latent variables and causal structures from observational data is essential to
many real-world applications involving biological data, medical data, and unstructured data …

Discovering causal relationships between variables is a difficult unsupervised learning task,
which becomes more challenging if there are unobserved common causes between pairs of …

The evolution of communication and a continued globalization process have resulted in
bigger quantities of data being storaged. However, data has not only increased in volume …