A graphical model is a statistical model that is associated with a graph whose nodes
correspond to variables of interest. The edges of the graph reflect allowed conditional …

We study the problem of maximizing a function that is approximately submodular under a
cardinality constraint. Approximate submodularity implicitly appears in a wide range of …

We consider the problem of learning a high-dimensional graphical model in which there are
a few hub nodes that are densely-connected to many other nodes. Many authors have …

Graphical models are widely used to model stochastic dependences among large
collections of variables. We introduce a new method of estimating undirected conditional …

SEIR (Susceptible-Exposed-Infected-Recovered) is a general and widely-used diffusion
model that can model the diffusion in different contexts such as idea spreading and disease …

Learning the structure of a probabilistic graphical models is a well studied problem in the
machine learning community due to its importance in many applications. Current …

Estimation of Markov Random Field and covariance models from high-dimensional data
represents a canonical problem that has received a lot of attention in the literature. A key …

Submodular functions have become a ubiquitous tool in machine learning. They are
learnable from data, and can be optimized efficiently and with guarantees. Nonetheless …

Aggregative games provide a rich abstraction to model strategic multi-agent interactions. We
focus on learning local aggregative games, where the payoff of each player is a function of …

Recovering network connectivity structure from high‐dimensional observations is of
increasing importance in statistical learning applications. A prominent approach is to learn a …