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High Dimensional Semiparametric Gaussian Copula Graphical Models
- Han Liu, F. Han, M. Yuan, J. Lafferty, L. Wasserman
- Computer Science, Mathematics
- 10 February 2012
ing the Spearman’s rho and Kendall’s tau. We prove that the nonparanormal skeptic achieves the optimal parametric rates of convergence for both graph recovery and parameter estimation. This result… Expand
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
Recent methods for estimating sparse undirected graphs for real-valued data in high dimensional problems rely heavily on the assumption of normality. We show how to use a semiparametric Gaussian… Expand
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models
A challenging problem in estimating high-dimensional graphical models is to choose the regularization parameter in a data-dependent way. The standard techniques include K-fold cross-validation… Expand
Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions
Classical stochastic gradient methods are well suited for minimizing expected-value objective functions. However, they do not apply to the minimization of a nonlinear function involving expected… Expand
Phosphorene: an unexplored 2D semiconductor with a high hole mobility.
We introduce the 2D counterpart of layered black phosphorus, which we call phosphorene, as an unexplored p-type semiconducting material. Same as graphene and MoS2, single-layer phosphorene is… Expand
SpAM: Sparse Additive Models
We present a new class of models for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and… Expand
A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models
We consider the problem of uncertainty assessment for low dimensional components in high dimensional models. Specifically, we propose a decorrelated score function to handle the impact of high… Expand
A Strictly Contractive Peaceman-Rachford Splitting Method for Convex Programming
In this paper, we focus on the application of the Peaceman-Rachford splitting method (PRSM) to a convex minimization model with linear constraints and a separable objective function. Compared to the… Expand
OPTIMAL COMPUTATIONAL AND STATISTICAL RATES OF CONVERGENCE FOR SPARSE NONCONVEX LEARNING PROBLEMS.
We provide theoretical analysis of the statistical and computational properties of penalized M-estimators that can be formulated as the solution to a possibly nonconvex optimization problem. Many… Expand
The huge Package for High-dimensional Undirected Graph Estimation in R
- Tuo Zhao, Han Liu, K. Roeder, J. Lafferty, L. Wasserman
- Mathematics, Computer Science
- J. Mach. Learn. Res.
- 1 March 2012
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 literature,… Expand