We provide a unified framework for establishing consistency and convergence rates for such regularized M-estimators under high-dimensional scaling, and show how it can be used to re-derive several existing results.Expand

Using an open-source, Java toolkit of name-matching methods, we experimentally compare string distance metrics on the task of matching entity names.Expand

Given i.i.d. observations of a random vector X 2 R p , we study the problem of estimating both its covariance matrix � ∗ , and its inverse covariance or concentration matrix � ∗ = (� ∗ ) −1 . We… Expand

We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on $\ell_1$-regularized logistic regression, in which the neighborhood… Expand

We present a new class of methods for high dimensional non-parametric regression and classification called sparse additive models. Our methods combine ideas from sparse linear modelling and additive… Expand

A powerful approach to probabilistic modelling involves supplementing a set of observed variables with additional latent, or hidden, variables. By defining a joint distribution over visible and… Expand

We propose a new approach for score-based learning of DAGs by converting the traditional combinatorial optimization problem (left) into a continuous program (right).Expand