Advances in optical manipulation and observation of neural activity have set the stage for
widespread implementation of closed-loop and activity-guided optical control of neural …

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 discuss the Gaussian graphical model (GGM; an undirected network of partial
correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM …

We consider the problem of estimating multiple related Gaussian graphical models from a
high dimensional data set with observations belonging to distinct classes. We propose the …

What do auto-encoders learn about the underlying data-generating distribution? Recent
work suggests that some auto-encoder variants do a good job of capturing the local manifold …

We consider rules for discarding predictors in lasso regression and related problems, for
computational efficiency. El Ghaoui and his colleagues have proposed 'SAFE'rules, based …

We describe an R package named huge which provides easy-to-use functions for estimating
high dimensional undirected graphs from data. This package implements recent results in …

The L_1 regularized Gaussian maximum likelihood estimator has been shown to have
strong statistical guarantees in recovering a sparse inverse covariance matrix, or …

This is an expository paper that reviews recent developments on optimal estimation of
structured high-dimensional covariance and precision matrices. Minimax rates of …

The l1-regularized Gaussian maximum likelihood estimator (MLE) has been shown to have
strong statistical guarantees in recovering a sparse inverse covariance matrix, or …